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To address the physical layer security challenges in low-altitude unmanned aerial vehicle (UAV) communications, this paper proposes an integrated sensing and communication (ISAC) scheme. For the proposed ISAC scheme, an online optimization framework for UAV trajectory and communication resource allocation is developed using deep reinforcement learning (DRL). In the proposed scheme, artificial noise transmitted by a communication UAV is reused to simultaneously sense and jam a potential eavesdropping UAV, thereby enhancing secure communications for ground users. By estimating and predicting the state of the eavesdropping UAV, the trajectory and resource allocation design problem is reformulated as a Markov decision process. Using the deep deterministic policy gradient algorithm, the optimal framework is learned over time, dynamically optimizing the communication UAV’s trajectory and resource allocation to maximize long-term sensing and secure communication performance. Simulation results demonstrate that the proposed scheme achieves a superior trade-off between sensing and security without degrading sensing performance and outperforms baseline methods in terms of secure communication performance. This validates the performance gains achieved through sensing and online trajectory design, as well as the potential and superior performance of applying DRL to the integrated design of sensing, communication, and trajectory.
To address the physical layer security challenges in low-altitude unmanned aerial vehicle (UAV) communications, this paper proposes an integrated sensing and communication (ISAC) scheme. For the proposed ISAC scheme, an online optimization framework for UAV trajectory and communication resource allocation is developed using deep reinforcement learning (DRL). In the proposed scheme, artificial noise transmitted by a communication UAV is reused to simultaneously sense and jam a potential eavesdropping UAV, thereby enhancing secure communications for ground users. By estimating and predicting the state of the eavesdropping UAV, the trajectory and resource allocation design problem is reformulated as a Markov decision process. Using the deep deterministic policy gradient algorithm, the optimal framework is learned over time, dynamically optimizing the communication UAV’s trajectory and resource allocation to maximize long-term sensing and secure communication performance. Simulation results demonstrate that the proposed scheme achieves a superior trade-off between sensing and security without degrading sensing performance and outperforms baseline methods in terms of secure communication performance. This validates the performance gains achieved through sensing and online trajectory design, as well as the potential and superior performance of applying DRL to the integrated design of sensing, communication, and trajectory.
A real aperture radar has physical space limitations that result in a wide antenna beam, leading to low angular resolution. The angular super-resolution method based on sparse reconstruction introduces sparse prior constraints of the target under a regularization framework and reconstructs the target reflectivity function through iterative optimization, thereby significantly enhancing the angular resolution of the radar. However, existing sparse reconstruction methods primarily consider the sparse distribution characteristics of strong point targets, neglecting the contour information of extended targets, which results in distortion in the recovery of target edges. Additionally, these methods are sensitive to one or more hyperparameters introduced into the cost function. Thus, meticulous manual adjustments are essential in practical applications, and they pose challenges in terms of the adaptive selection of hyperparameters in dynamic scenarios. To address these issues, this paper proposes a hyperparameter-free Total Variation (TV) regularization angular super-resolution method. First, a square-root Least Absolute Shrinkage and Selection Operator (LASSO) cost function was established to characterize the fitting residuals between the scan echo sequence and target reflectivity function and to characterize the sparse constraints on the target edge gradients. Using this function, the target contour reconstruction problem was transformed into a non-smooth convex optimization problem under TV regularization constraints. The analytical expression of the hyperparameter-free TV regularization term was derived based on the covariance fitting criterion. Finally, a Generalized Iteratively Reweighted Least Squares (GIRLS) strategy was proposed, and an iterative optimization method for solving the non-smooth convex optimization problem of square-root LASSO was derived. The simulation and experimental results demonstrate that the proposed method improves angular resolution of the radar while preserving the contour information of the target without requiring manual adjustment of the hyperparameters.
A real aperture radar has physical space limitations that result in a wide antenna beam, leading to low angular resolution. The angular super-resolution method based on sparse reconstruction introduces sparse prior constraints of the target under a regularization framework and reconstructs the target reflectivity function through iterative optimization, thereby significantly enhancing the angular resolution of the radar. However, existing sparse reconstruction methods primarily consider the sparse distribution characteristics of strong point targets, neglecting the contour information of extended targets, which results in distortion in the recovery of target edges. Additionally, these methods are sensitive to one or more hyperparameters introduced into the cost function. Thus, meticulous manual adjustments are essential in practical applications, and they pose challenges in terms of the adaptive selection of hyperparameters in dynamic scenarios. To address these issues, this paper proposes a hyperparameter-free Total Variation (TV) regularization angular super-resolution method. First, a square-root Least Absolute Shrinkage and Selection Operator (LASSO) cost function was established to characterize the fitting residuals between the scan echo sequence and target reflectivity function and to characterize the sparse constraints on the target edge gradients. Using this function, the target contour reconstruction problem was transformed into a non-smooth convex optimization problem under TV regularization constraints. The analytical expression of the hyperparameter-free TV regularization term was derived based on the covariance fitting criterion. Finally, a Generalized Iteratively Reweighted Least Squares (GIRLS) strategy was proposed, and an iterative optimization method for solving the non-smooth convex optimization problem of square-root LASSO was derived. The simulation and experimental results demonstrate that the proposed method improves angular resolution of the radar while preserving the contour information of the target without requiring manual adjustment of the hyperparameters.
To address the low data rate issue in the design of Dual-Function Radar and Communication (DFRC) waveforms with radar detection as the primary function, this paper proposes an information modulation method for multiple sub-pulse structure waveforms called Sub-pulse Hybrid Modulation (SHM). The proposed SHM method utilizes time-, spectral-, and polarization-domain features from inter-subpulse and intra-subpulse sources to convey information. The DFRC waveform design problem is formulated based on minimizing cross- and auto-correlation peak sidelobe levels, while considering constant envelope and SHM constraints. To tackle the resulting nonconvex and nondeterministic polynomial-hard optimization problem, the spectral majorization-minimization algorithm is employed to monotonically decrease the objective function value. Furthermore, this paper explores an echo processing method that makes the Doppler frequency at the first zero point of the zero-delay intercept of the fuzzy function \begin{document}$ L - 1 $\end{document} \begin{document}$ L - 1 $\end{document}
When radar and communication systems share the same frequency spectrum on the same platform, mutual interference may occur. In addition, mainlobe deceptive interferences pose a serious threat to radar target detection. To address these issues, we devise a Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar and MIMO communication spectral coexistence system and propose a radar-centric joint transceiver design scheme. In this respect, the radar transmission waveform, radar receive filter, and communication transmission codebook are optimized to maximize the Signal-to-Interference-plus-Noise Ratio (SINR) of the radar system, thereby enhancing the target detection probability while ensuring MIMO communication throughput. During the optimization process, the Alternating Optimization (AO) strategy is employed to decompose the problem into multiple subproblems, which are solved in an iterative way. Specifically, the radar receive filter is obtained using the Lagrange multiplier method. In addition, the communication transmission codebook is approximated using an inequality theorem, and the radar transmission waveform is optimized using Taylor expansion and relaxation algorithms. Simulation results reveal that this joint design method can effectively improve the SINR of the radar system while ensuring communication throughput, thereby considerably enhancing the performance of the FDA-MIMO radar and MIMO communication spectral coexistence system under mainlobe jamming conditions.
When radar and communication systems share the same frequency spectrum on the same platform, mutual interference may occur. In addition, mainlobe deceptive interferences pose a serious threat to radar target detection. To address these issues, we devise a Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar and MIMO communication spectral coexistence system and propose a radar-centric joint transceiver design scheme. In this respect, the radar transmission waveform, radar receive filter, and communication transmission codebook are optimized to maximize the Signal-to-Interference-plus-Noise Ratio (SINR) of the radar system, thereby enhancing the target detection probability while ensuring MIMO communication throughput. During the optimization process, the Alternating Optimization (AO) strategy is employed to decompose the problem into multiple subproblems, which are solved in an iterative way. Specifically, the radar receive filter is obtained using the Lagrange multiplier method. In addition, the communication transmission codebook is approximated using an inequality theorem, and the radar transmission waveform is optimized using Taylor expansion and relaxation algorithms. Simulation results reveal that this joint design method can effectively improve the SINR of the radar system while ensuring communication throughput, thereby considerably enhancing the performance of the FDA-MIMO radar and MIMO communication spectral coexistence system under mainlobe jamming conditions.
Conventional spaceborne monostatic radar systems incur huge engineering costs to achieve small moving-target detection and low anti-interference ability. By manipulating the transmitter-receiver separation in a spaceborne bistatic radar system, the target radar cross section can be effectively improved by adopting a configuration with a large azimuth bistatic angle, and the anti-interference ability can be improved because the receiver does not transmit signals. However, the characteristics of the background clutter echo in a spaceborne bistatic radar system differ drastically from those in a spaceborne monostatic radar system because of the transmitter-receiver separation in the former. To overcome the limitations of existing empirical clutter scattering coefficient models, which typically do not capture the variation of scattering coefficient with azimuth bistatic angle, this study proposes a semiempirical bistatic clutter scattering coefficient model based on the two-scale model. In the proposed model, an empirical clutter backscattering coefficient model can be converted to a bistatic clutter scattering coefficient model based on electromagnetic scattering theories, and the bistatic scattering coefficient is further modified based on the two-scale model. The proposed model was validated using real measured data of bistatic clutter scattering coefficients obtained from existing literature. Using the proposed model, clutter suppression performance under different azimuth bistatic angles was analyzed by employing space-time adaptive processing in spaceborne bistatic radar systems. Reportedly, under HH polarization, the clutter suppression performance was relatively good when the azimuth bistatic angle was 30°～130°, whereas the clutter suppression performance was considerably affected by large-power main-lobe clutter when the azimuth bistatic angle was >150°.
Conventional spaceborne monostatic radar systems incur huge engineering costs to achieve small moving-target detection and low anti-interference ability. By manipulating the transmitter-receiver separation in a spaceborne bistatic radar system, the target radar cross section can be effectively improved by adopting a configuration with a large azimuth bistatic angle, and the anti-interference ability can be improved because the receiver does not transmit signals. However, the characteristics of the background clutter echo in a spaceborne bistatic radar system differ drastically from those in a spaceborne monostatic radar system because of the transmitter-receiver separation in the former. To overcome the limitations of existing empirical clutter scattering coefficient models, which typically do not capture the variation of scattering coefficient with azimuth bistatic angle, this study proposes a semiempirical bistatic clutter scattering coefficient model based on the two-scale model. In the proposed model, an empirical clutter backscattering coefficient model can be converted to a bistatic clutter scattering coefficient model based on electromagnetic scattering theories, and the bistatic scattering coefficient is further modified based on the two-scale model. The proposed model was validated using real measured data of bistatic clutter scattering coefficients obtained from existing literature. Using the proposed model, clutter suppression performance under different azimuth bistatic angles was analyzed by employing space-time adaptive processing in spaceborne bistatic radar systems. Reportedly, under HH polarization, the clutter suppression performance was relatively good when the azimuth bistatic angle was 30°～130°, whereas the clutter suppression performance was considerably affected by large-power main-lobe clutter when the azimuth bistatic angle was >150°.
In the field of radar target recognition, the introduction of Icosahedron Triangular Trihedral Corner Reflector (ITTCR) has increased the difficulty of target identification tasks, especially under moderate to high sea states. Under such conditions, the undulating sea surface can couple with an ITTCR to produce scattering characteristics similar to those of the target, resulting in a decline in the performance of traditional target identification methods. As a solution, a joint matrix of polarization features and range was constructed by considering the dominant scattering mechanisms and scattering complexity. This matrix characterizes the component-level differences between ships and ITTCR arrays in the presence of sea clutter. Subsequently, a temporal neural network extracts features from the joint matrices of the vessels and ITTCR arrays, achieving effective target identification. The performance of the proposed method was validated using a dataset. The proposed method effectively reduces information loss during manual knowledge refinement. Under moderate to high sea states, the proposed method has an accuracy higher than that of the existing methods by 10.14%. Furthermore, the proposed method considerably reduces false alarms caused by ITTCR arrays.
In the field of radar target recognition, the introduction of Icosahedron Triangular Trihedral Corner Reflector (ITTCR) has increased the difficulty of target identification tasks, especially under moderate to high sea states. Under such conditions, the undulating sea surface can couple with an ITTCR to produce scattering characteristics similar to those of the target, resulting in a decline in the performance of traditional target identification methods. As a solution, a joint matrix of polarization features and range was constructed by considering the dominant scattering mechanisms and scattering complexity. This matrix characterizes the component-level differences between ships and ITTCR arrays in the presence of sea clutter. Subsequently, a temporal neural network extracts features from the joint matrices of the vessels and ITTCR arrays, achieving effective target identification. The performance of the proposed method was validated using a dataset. The proposed method effectively reduces information loss during manual knowledge refinement. Under moderate to high sea states, the proposed method has an accuracy higher than that of the existing methods by 10.14%. Furthermore, the proposed method considerably reduces false alarms caused by ITTCR arrays.
Spaceborne Synthetic Aperture Radar (SAR) data may be prone to interrupted-sampling repeater jamming and many common unintentional interferences, such as linear frequency modulated pulses. In this paper, we first divide a single-look complex SAR image into multiple sub-band images of equal bandwidth in the range frequency domain. Then, we model the pixel intensity of these sub-band images and analyze the fluctuation mechanism of interfering and noninterfering pixels across the subbands. The findings reveal that the energy distribution of interfering pixels is uneven across different sub-bands, leading to substantial intensity fluctuations within the sub-band domain, whereas the intensity of noninterfering pixels remains relatively stable. Based on this observation, we define sub-band contrast and sub-band entropy as statistical measures to quantify fluctuation characteristics across the sub-bands. These measures are then compared with certain thresholds to obtain detection results. Statistical analysis revealed that under noninterfering conditions, these two statistics approximately follow the beta distribution. By leveraging this finding, we fit the distributions of these measures using the beta distribution and develop a method to determine detection thresholds under the constant-false-alarm-rate criterion. Experimental results showed that the proposed method can effectively detect interrupted-sampling repeater jamming and common unintentional interferences. In addition, we investigated the impact of the jamming-to-signal ratio on detection performance and verified the reliability and stability of the method via Monte Carlo simulations. Furthermore, we introduced an interference suppression technique based on a rank-1 model to reduce the adverse effects of interference on downstream tasks. This technique is capable of adaptively suppressing interference in detected regions.
Spaceborne Synthetic Aperture Radar (SAR) data may be prone to interrupted-sampling repeater jamming and many common unintentional interferences, such as linear frequency modulated pulses. In this paper, we first divide a single-look complex SAR image into multiple sub-band images of equal bandwidth in the range frequency domain. Then, we model the pixel intensity of these sub-band images and analyze the fluctuation mechanism of interfering and noninterfering pixels across the subbands. The findings reveal that the energy distribution of interfering pixels is uneven across different sub-bands, leading to substantial intensity fluctuations within the sub-band domain, whereas the intensity of noninterfering pixels remains relatively stable. Based on this observation, we define sub-band contrast and sub-band entropy as statistical measures to quantify fluctuation characteristics across the sub-bands. These measures are then compared with certain thresholds to obtain detection results. Statistical analysis revealed that under noninterfering conditions, these two statistics approximately follow the beta distribution. By leveraging this finding, we fit the distributions of these measures using the beta distribution and develop a method to determine detection thresholds under the constant-false-alarm-rate criterion. Experimental results showed that the proposed method can effectively detect interrupted-sampling repeater jamming and common unintentional interferences. In addition, we investigated the impact of the jamming-to-signal ratio on detection performance and verified the reliability and stability of the method via Monte Carlo simulations. Furthermore, we introduced an interference suppression technique based on a rank-1 model to reduce the adverse effects of interference on downstream tasks. This technique is capable of adaptively suppressing interference in detected regions.
Efficient radio-frequency (RF) stealth is crucial for dual-function radar-communication (DFRC) systems that detect radar stealth and con vert communication transmission. However, traditional beamforming schemes based on phased arrays and multiple-input multiple-output (MIMO) systems lack the ability to control the radiation energy in the range dimension, resulting in the facile interception of integrated transmission signals by enemy-owned passive detection systems. To address this issue, a joint transmit-receive beamforming design for frequency diversity array MIMO (FDA-MIMO) DFRC systems is designed herein to achieve RF stealth. First, an integrated transmission signal model based on orthogonal waveform generation, frequency diversity modulation, and weighted transmission beamforming is constructed. The two-dimensional expression of the distance angle between the radar equivalent transmission beam pattern and the communication transmission channel is obtained through matched filtering and reception beamforming. Second, with communication information embedding and communication reachable rate as constraints, a joint optimization model for FDA-MIMO radar communication integrated transmission and reception beams for RF stealth is established. The model aims to simultaneously minimize the equivalent transmission beam power at the radar target and maximize the output signal-to-noise ratio. Finally, a joint optimization algorithm based on weighted mean square error minimization and the consensus alternating direction multiplier method is proposed. Closed form expressions for each variable are derived and combined with convex optimization algorithms to achieve low-complexity solutions. The simulation results show that radar detection and communication transmission using the proposed method form a “point-to-point” pattern on the two-dimensional plane of range and angle, exhibiting good RF stealth capability. Simultaneously, this method can provide high clutter and interference suppression performance as well as a low communication bit error rate.
Efficient radio-frequency (RF) stealth is crucial for dual-function radar-communication (DFRC) systems that detect radar stealth and con vert communication transmission. However, traditional beamforming schemes based on phased arrays and multiple-input multiple-output (MIMO) systems lack the ability to control the radiation energy in the range dimension, resulting in the facile interception of integrated transmission signals by enemy-owned passive detection systems. To address this issue, a joint transmit-receive beamforming design for frequency diversity array MIMO (FDA-MIMO) DFRC systems is designed herein to achieve RF stealth. First, an integrated transmission signal model based on orthogonal waveform generation, frequency diversity modulation, and weighted transmission beamforming is constructed. The two-dimensional expression of the distance angle between the radar equivalent transmission beam pattern and the communication transmission channel is obtained through matched filtering and reception beamforming. Second, with communication information embedding and communication reachable rate as constraints, a joint optimization model for FDA-MIMO radar communication integrated transmission and reception beams for RF stealth is established. The model aims to simultaneously minimize the equivalent transmission beam power at the radar target and maximize the output signal-to-noise ratio. Finally, a joint optimization algorithm based on weighted mean square error minimization and the consensus alternating direction multiplier method is proposed. Closed form expressions for each variable are derived and combined with convex optimization algorithms to achieve low-complexity solutions. The simulation results show that radar detection and communication transmission using the proposed method form a “point-to-point” pattern on the two-dimensional plane of range and angle, exhibiting good RF stealth capability. Simultaneously, this method can provide high clutter and interference suppression performance as well as a low communication bit error rate.
In radar systems that track multiple maneuvering targets, conventional approaches often suffer from performance degradation due to suboptimal resource allocation and insufficient utilization of prior information. To address this challenge and significantly enhance tracking performance under equivalent resource constraints, a resource allocation and precise tracking algorithm for multiple maneuvering targets is proposed. First, by integrating a multimodel interaction architecture with tracker feedback prediction, a probabilistic distribution model for target position prediction through multiple model interaction is constructed. This model establishes an integrated detection and tracking method based on multiple model interactions to achieve refined tracking of maneuvering targets. Subsequently, by analytically modeling the coupling mechanism between radar resources and tracking performance, and deriving the Bayesian Cramér-Rao lower bound for maneuvering targets, a performance-driven multimodel weighted resource allocation framework is developed. Simulations validate that the proposed method can significantly enhance the overall tracking precision of multiple maneuvering targets under equivalent resource consumption.
In radar systems that track multiple maneuvering targets, conventional approaches often suffer from performance degradation due to suboptimal resource allocation and insufficient utilization of prior information. To address this challenge and significantly enhance tracking performance under equivalent resource constraints, a resource allocation and precise tracking algorithm for multiple maneuvering targets is proposed. First, by integrating a multimodel interaction architecture with tracker feedback prediction, a probabilistic distribution model for target position prediction through multiple model interaction is constructed. This model establishes an integrated detection and tracking method based on multiple model interactions to achieve refined tracking of maneuvering targets. Subsequently, by analytically modeling the coupling mechanism between radar resources and tracking performance, and deriving the Bayesian Cramér-Rao lower bound for maneuvering targets, a performance-driven multimodel weighted resource allocation framework is developed. Simulations validate that the proposed method can significantly enhance the overall tracking precision of multiple maneuvering targets under equivalent resource consumption.
The integration technology of satellite communications and spaceborne Synthetic Aperture Radar (SAR) remote sensing aims to combine communication and remote sensing functionalities, enabling simultaneous data transmission and remote sensing imaging to meet the demands of efficient, covert, and secure information transfer, enhancing system multifunctionality. However, due to significant differences between the waveform characteristics, transceiver design, and signal processing algorithms of the technologies, integrating communication and remote sensing in spaceborne systems presents numerous challenges. This study proposes a passive wireless communication system based on information metasurface technology, combined with SAR echo modulation methods, to innovatively achieve the deep integration of ground-to-space communication and spaceborne SAR remote sensing. The system precisely modulates the scattering parameters of SAR echoes to enable passive wireless communication without affecting the quality of SAR imaging. Additionally, by leveraging electromagnetic backscatter properties instead of active transmission mechanisms, the system effectively ensures the electromagnetic concealment and information security of the communication link. A series of scene simulation experiments and spaceborne SAR data experiments were performed, which validated the system’s feasibility and effectiveness. Results indicate that while maintaining compatibility with traditional SAR waveform structures, the proposed system successfully achieved the synchronous operation of ground-to-space data transmission and spaceborne SAR imaging. The core objective of this research was to promote the deep integration of spaceborne SAR remote sensing systems and wireless communication technologies, aiming for the efficient utilization of spectrum resources and exploring the application of information metasurface technology in communication-remote sensing integrated systems, providing new research perspectives and technological potential in this field.
The integration technology of satellite communications and spaceborne Synthetic Aperture Radar (SAR) remote sensing aims to combine communication and remote sensing functionalities, enabling simultaneous data transmission and remote sensing imaging to meet the demands of efficient, covert, and secure information transfer, enhancing system multifunctionality. However, due to significant differences between the waveform characteristics, transceiver design, and signal processing algorithms of the technologies, integrating communication and remote sensing in spaceborne systems presents numerous challenges. This study proposes a passive wireless communication system based on information metasurface technology, combined with SAR echo modulation methods, to innovatively achieve the deep integration of ground-to-space communication and spaceborne SAR remote sensing. The system precisely modulates the scattering parameters of SAR echoes to enable passive wireless communication without affecting the quality of SAR imaging. Additionally, by leveraging electromagnetic backscatter properties instead of active transmission mechanisms, the system effectively ensures the electromagnetic concealment and information security of the communication link. A series of scene simulation experiments and spaceborne SAR data experiments were performed, which validated the system’s feasibility and effectiveness. Results indicate that while maintaining compatibility with traditional SAR waveform structures, the proposed system successfully achieved the synchronous operation of ground-to-space data transmission and spaceborne SAR imaging. The core objective of this research was to promote the deep integration of spaceborne SAR remote sensing systems and wireless communication technologies, aiming for the efficient utilization of spectrum resources and exploring the application of information metasurface technology in communication-remote sensing integrated systems, providing new research perspectives and technological potential in this field.
The measurement from automotive millimeter-wave radar consists of position coordinates in the polar coordinate system and doppler velocity, which has a complex, nonlinear relationship with the extended object state modeled in the Cartesian coordinate system. To address this nonlinear state estimation problem, a Variational Marginalized Particle Filter-based Extended Object Tracking (VMPF-EOT) algorithm is proposed. First, the object’s two-dimensional planar contour is modeled as an ellipse with an explicitly defined orientation angle. A parameterized inverse gamma distribution is constructed as the conjugate prior distribution for the contour size. Second, the measurement source position is introduced as an auxiliary variable to establish a measurement model for extended objects detected by automotive millimeter-wave radar. To enhance the contour estimation performance for maneuvering objects, the joint distribution of the extended object state is marginalized with respect to the contour orientation angle. The posterior distribution of the contour orientation angle is estimated independently using a particle filter. The approximate analytical solution for the posterior distributions of the remaining state variables—including the target’s center motion state and contour size—is derived using the variational Bayesian inference. The simulation results demonstrate that the proposed algorithm achieves higher state estimation accuracy than existing algorithms. In tracking maneuvering targets, the proposed algorithm offers a more significant advantage in terms of estimating the contour orientation angle and contour size.
The measurement from automotive millimeter-wave radar consists of position coordinates in the polar coordinate system and doppler velocity, which has a complex, nonlinear relationship with the extended object state modeled in the Cartesian coordinate system. To address this nonlinear state estimation problem, a Variational Marginalized Particle Filter-based Extended Object Tracking (VMPF-EOT) algorithm is proposed. First, the object’s two-dimensional planar contour is modeled as an ellipse with an explicitly defined orientation angle. A parameterized inverse gamma distribution is constructed as the conjugate prior distribution for the contour size. Second, the measurement source position is introduced as an auxiliary variable to establish a measurement model for extended objects detected by automotive millimeter-wave radar. To enhance the contour estimation performance for maneuvering objects, the joint distribution of the extended object state is marginalized with respect to the contour orientation angle. The posterior distribution of the contour orientation angle is estimated independently using a particle filter. The approximate analytical solution for the posterior distributions of the remaining state variables—including the target’s center motion state and contour size—is derived using the variational Bayesian inference. The simulation results demonstrate that the proposed algorithm achieves higher state estimation accuracy than existing algorithms. In tracking maneuvering targets, the proposed algorithm offers a more significant advantage in terms of estimating the contour orientation angle and contour size.
The development of intelligent jamming decision-making technology has substantially enhanced the survival and confrontation capabilities of sensitive targets on the battlefield. However, existing jamming decision-making algorithms only consider active jamming while neglecting the optimization of passive jamming strategies. This limitation seriously restricts the application of adversarial models in jamming decision-making scenarios. Aiming to address this defect, this paper constructs a joint optimization method for active-passive jamming strategies based on Rainbow Deep Q-Network (DQN) and dichotomy. The method uses Rainbow DQN to determine the sequence of active and passive jamming styles and applies a dichotomy to dynamically search for the optimal release position of passive jamming. Additionally, considering the partially observable nature of the jamming confrontation environment, this paper further designs an optimization method for active-passive jamming strategies based on Rainbow DQN and Baseline DQN. A reward function is also introduced, based on changes in the radar beam pointing point, to accurately feedback the effectiveness of the jamming strategy. Through simulation experiments in jammer-radar confrontations, the proposed method is compared with the following three mainstream jamming decision models: Baseline DQN, Dueling DQN, and Double DQN. Results show that, compared to other interference decision-making models, the proposed method improves the Q value by an average of 2.43 times, the reward mean value by an average of 3.09 times, and reduces the number of decision-making steps for passive interference location by more than 50%. The experimental results show that the proposed joint active-passive jamming strategy optimization method based on Rainbow DQN and dichotomy substantially enhances the effectiveness of decision-making, improving the applicability of jamming strategy models and drastically boosting the value of the jammer in electronic countermeasures.
The development of intelligent jamming decision-making technology has substantially enhanced the survival and confrontation capabilities of sensitive targets on the battlefield. However, existing jamming decision-making algorithms only consider active jamming while neglecting the optimization of passive jamming strategies. This limitation seriously restricts the application of adversarial models in jamming decision-making scenarios. Aiming to address this defect, this paper constructs a joint optimization method for active-passive jamming strategies based on Rainbow Deep Q-Network (DQN) and dichotomy. The method uses Rainbow DQN to determine the sequence of active and passive jamming styles and applies a dichotomy to dynamically search for the optimal release position of passive jamming. Additionally, considering the partially observable nature of the jamming confrontation environment, this paper further designs an optimization method for active-passive jamming strategies based on Rainbow DQN and Baseline DQN. A reward function is also introduced, based on changes in the radar beam pointing point, to accurately feedback the effectiveness of the jamming strategy. Through simulation experiments in jammer-radar confrontations, the proposed method is compared with the following three mainstream jamming decision models: Baseline DQN, Dueling DQN, and Double DQN. Results show that, compared to other interference decision-making models, the proposed method improves the Q value by an average of 2.43 times, the reward mean value by an average of 3.09 times, and reduces the number of decision-making steps for passive interference location by more than 50%. The experimental results show that the proposed joint active-passive jamming strategy optimization method based on Rainbow DQN and dichotomy substantially enhances the effectiveness of decision-making, improving the applicability of jamming strategy models and drastically boosting the value of the jammer in electronic countermeasures.
Aiming to enhance sensing resolution and improve spectral efficiency, future Integrated Sensing And Communications (ISAC) systems are expected to incorporate extremely large-scale (XL) arrays and large bandwidths centered around high carrier frequencies. This design necessitates considering wideband and near-field effects. In this paper, the design of partially connected hybrid precoders for ISAC is refined and evaluated, focusing on the wideband near-field scenario and addressing monostatic and bistatic co-located Multiple-Input-Multiple-Output (MIMO). For monostatic MIMO, the Cramer-Rao Bound (CRB) for joint Direction-of-Arrival (DoA) and distance estimations of sensing wideband sources is rederived, serving as a performance metric for sensing. For bistatic MIMO, power irradiated at near-field targets is maximized while ensuring that the communication Quality of Service (QoS) for each user is maintained. To address the above nonconvex, high-dimensional problems, a direct alternative minimization, along with an indirect fully digital approximation, is proposed. This method decomposes the original problems into distinct subproblems, enabling effective solutions for each subproblem. Simulation results demonstrate that the proposed wideband near-field ISAC framework can achieve sensing and communication performance close to that of fully digital precoders, given an appropriate communication Signal-to-Noise Ratio (SNR) setting and transmit antenna grouping.
Aiming to enhance sensing resolution and improve spectral efficiency, future Integrated Sensing And Communications (ISAC) systems are expected to incorporate extremely large-scale (XL) arrays and large bandwidths centered around high carrier frequencies. This design necessitates considering wideband and near-field effects. In this paper, the design of partially connected hybrid precoders for ISAC is refined and evaluated, focusing on the wideband near-field scenario and addressing monostatic and bistatic co-located Multiple-Input-Multiple-Output (MIMO). For monostatic MIMO, the Cramer-Rao Bound (CRB) for joint Direction-of-Arrival (DoA) and distance estimations of sensing wideband sources is rederived, serving as a performance metric for sensing. For bistatic MIMO, power irradiated at near-field targets is maximized while ensuring that the communication Quality of Service (QoS) for each user is maintained. To address the above nonconvex, high-dimensional problems, a direct alternative minimization, along with an indirect fully digital approximation, is proposed. This method decomposes the original problems into distinct subproblems, enabling effective solutions for each subproblem. Simulation results demonstrate that the proposed wideband near-field ISAC framework can achieve sensing and communication performance close to that of fully digital precoders, given an appropriate communication Signal-to-Noise Ratio (SNR) setting and transmit antenna grouping.
In an increasingly complex electromagnetic environment, the composite detection of active-passive radar, with its excellent complementary advantages, has become an important working mode for enhancing the combat capability and anti-interference capability of radars. The traditional single suppression or deception jamming method can only produce effective jamming in active or passive radar mode, and a good jamming effect is difficult to produce on the composite detection of active-passive radar. In order to improve the jamming ability of active-passive radar composite detection, this paper proposes a full-pulse multi-jammer cooperative jamming method for active-passive radar composite detection. By analyzing the principle of Constant False Alarm Rate (CFAR) detection in radar active mode, the power series and position spacing distribution of multiple false targets is adjusted through the correlation between radar detection probability and signal-to-noise ratio, and the full-pulse time-domain rendering covert jamming model is constructed to effectively suppress the radar active mode. At the same time, by analyzing the principle of angle direction finding in radar passive mode, a cooperative jamming strategy based on multiple jammers is proposed, which dynamically adjusts the transmitting power of jammers and generates multiple random deception angles among the jammers to realize the multi-angle deception effect in radar passive mode. Finally, through the organic combination of the aforementioned two strategies, a full-pulse multi-jammer cooperative jamming method is constructed to achieve effective jamming in active-passive radar composite detection. The experimental results show that compared with the traditional single suppression or deception jamming methods, the proposed full-pulse multi-jammer cooperative jamming method can effectively increase the detection threshold of radar CFAR and, reduce the detection probability in the radar active mode. At the same time, different false angles are generated in each frame near the jammer to expand the range of angle deception, to comprehensively improve the jamming performance of active-passive radar composite detection.
In an increasingly complex electromagnetic environment, the composite detection of active-passive radar, with its excellent complementary advantages, has become an important working mode for enhancing the combat capability and anti-interference capability of radars. The traditional single suppression or deception jamming method can only produce effective jamming in active or passive radar mode, and a good jamming effect is difficult to produce on the composite detection of active-passive radar. In order to improve the jamming ability of active-passive radar composite detection, this paper proposes a full-pulse multi-jammer cooperative jamming method for active-passive radar composite detection. By analyzing the principle of Constant False Alarm Rate (CFAR) detection in radar active mode, the power series and position spacing distribution of multiple false targets is adjusted through the correlation between radar detection probability and signal-to-noise ratio, and the full-pulse time-domain rendering covert jamming model is constructed to effectively suppress the radar active mode. At the same time, by analyzing the principle of angle direction finding in radar passive mode, a cooperative jamming strategy based on multiple jammers is proposed, which dynamically adjusts the transmitting power of jammers and generates multiple random deception angles among the jammers to realize the multi-angle deception effect in radar passive mode. Finally, through the organic combination of the aforementioned two strategies, a full-pulse multi-jammer cooperative jamming method is constructed to achieve effective jamming in active-passive radar composite detection. The experimental results show that compared with the traditional single suppression or deception jamming methods, the proposed full-pulse multi-jammer cooperative jamming method can effectively increase the detection threshold of radar CFAR and, reduce the detection probability in the radar active mode. At the same time, different false angles are generated in each frame near the jammer to expand the range of angle deception, to comprehensively improve the jamming performance of active-passive radar composite detection.
The paper proposes a double hierarchical nonhomogeneous multirank target detection method for the distributed Multiple-Input Multiple-Output (MIMO) radar to detect targets in scenarios with subspace interference and nonhomogeneous clutter. First, a multirank target model and subspace interference model are established based on the fact that the target signal and interference are located in two linearly independent subspaces, each with a rank greater than 1. The corresponding subspace matrices of the two subspaces and the coordinate vectors of the corresponding distance units are unknown. Then, a distributed MIMO radar system with a double hierarchical nonhomogeneous structure is designed, and the interference of each transmit-receive pair is nonhomogeneous, i.e., each transmit-receive pair possesses different statistics. In addition, the clutter in one transmit-receive pair is nonhomogeneous. Subsequently, the double hierarchical nonhomogeneous multirank target Rao detector and Wald detector are designed for the distributed MIMO radar in the context of subspace interference. This is achieved by adopting the Rao and Wald test criteria, constructing the parameter estimation strategy to be solved, and estimating the power median normalized covariance. Theoretical derivation showed that the proposed method had a constant false alarm property for the clutter covariance matrix structure. Simulation experiments showed that the proposed method guarantees a constant false alarm property for the clutter covariance matrix structure; in addition, compared with the existing distributed MIMO radar detection methods, the proposed detection method improves the target detection and interference suppression performances.
The paper proposes a double hierarchical nonhomogeneous multirank target detection method for the distributed Multiple-Input Multiple-Output (MIMO) radar to detect targets in scenarios with subspace interference and nonhomogeneous clutter. First, a multirank target model and subspace interference model are established based on the fact that the target signal and interference are located in two linearly independent subspaces, each with a rank greater than 1. The corresponding subspace matrices of the two subspaces and the coordinate vectors of the corresponding distance units are unknown. Then, a distributed MIMO radar system with a double hierarchical nonhomogeneous structure is designed, and the interference of each transmit-receive pair is nonhomogeneous, i.e., each transmit-receive pair possesses different statistics. In addition, the clutter in one transmit-receive pair is nonhomogeneous. Subsequently, the double hierarchical nonhomogeneous multirank target Rao detector and Wald detector are designed for the distributed MIMO radar in the context of subspace interference. This is achieved by adopting the Rao and Wald test criteria, constructing the parameter estimation strategy to be solved, and estimating the power median normalized covariance. Theoretical derivation showed that the proposed method had a constant false alarm property for the clutter covariance matrix structure. Simulation experiments showed that the proposed method guarantees a constant false alarm property for the clutter covariance matrix structure; in addition, compared with the existing distributed MIMO radar detection methods, the proposed detection method improves the target detection and interference suppression performances.
Transmit coherence synthesis of the target of interest is crucial for achieving full coherence in distributed coherent aperture radars. Interrupted-Sampling Repeater Jamming (ISRJ) in the coherent parameter estimation phase poses great difficulties in transmitting coherence completely. To solve this issue, an interference suppression method based on ISRJ matched filtering features is proposed. This method can overcome the limitations of time-frequency domain filtering under low Jamming-to-Noise Ratio (JNR) conditions while providing a more accurate means of estimating interference parameters for interference reconstruction and cancellation under high JNR conditions. Simulation results showed that the proposed method achieved a significant suppression effect on ISRJ. At low JNRs, the probability of target detection increased by over 40% compared with other methods such as time-frequency domain filtering. At high JNRs, the equivalent signal-to-jamming ratio improved by more than 2.5 dB relative to other approaches.
Transmit coherence synthesis of the target of interest is crucial for achieving full coherence in distributed coherent aperture radars. Interrupted-Sampling Repeater Jamming (ISRJ) in the coherent parameter estimation phase poses great difficulties in transmitting coherence completely. To solve this issue, an interference suppression method based on ISRJ matched filtering features is proposed. This method can overcome the limitations of time-frequency domain filtering under low Jamming-to-Noise Ratio (JNR) conditions while providing a more accurate means of estimating interference parameters for interference reconstruction and cancellation under high JNR conditions. Simulation results showed that the proposed method achieved a significant suppression effect on ISRJ. At low JNRs, the probability of target detection increased by over 40% compared with other methods such as time-frequency domain filtering. At high JNRs, the equivalent signal-to-jamming ratio improved by more than 2.5 dB relative to other approaches.
Clutter suppression is an important technology for moving target indication. However, for Bistatic Synthetic Aperture Radar (BiSAR) moving target indication, traditional space-time adaptive processing and displaced phase center antenna methods cannot achieve the expected clutter suppression because of the strong coupling nonlinearity and nonstationarity of clutter. To address the aforementioned challenge, this study proposes a dual-channel clutter cancellation processing method via space-time decoupling for airborne BiSAR. The core lies in establishing the space-time decoupling matrix, which converts the strongly coupled nonlinear two-dimensional space-time spectrum of airborne BiSAR into that with consistent spatial frequency. The proposed method mainly consists of the following steps: (1) To improve the signal-to-clutter-plus-noise ratio of moving targets, the first-order Keystone transformation and high-order range migration correction function are applied to concentrate the energy of moving targets in the same range cell. (2) To weaken the azimuth spectrum expansion effect caused by the motion of bistatic platforms, the Doppler frequency rate term is compensated for each range cell. (3) To achieve clutter cancellation, the space-time decoupling matrix is introduced. The normalized Doppler frequency remains unchanged, and the clutter atoms on the airborne BiSAR space-time plane are linearly transformed into atomic positions with the same normalized spatial frequency. Then, the echo signals of dual channels are subtracted for effective clutter suppression. The effectiveness of the proposed method for airborne BiSAR clutter suppression is demonstrated through simulation and real data processing.
Clutter suppression is an important technology for moving target indication. However, for Bistatic Synthetic Aperture Radar (BiSAR) moving target indication, traditional space-time adaptive processing and displaced phase center antenna methods cannot achieve the expected clutter suppression because of the strong coupling nonlinearity and nonstationarity of clutter. To address the aforementioned challenge, this study proposes a dual-channel clutter cancellation processing method via space-time decoupling for airborne BiSAR. The core lies in establishing the space-time decoupling matrix, which converts the strongly coupled nonlinear two-dimensional space-time spectrum of airborne BiSAR into that with consistent spatial frequency. The proposed method mainly consists of the following steps: (1) To improve the signal-to-clutter-plus-noise ratio of moving targets, the first-order Keystone transformation and high-order range migration correction function are applied to concentrate the energy of moving targets in the same range cell. (2) To weaken the azimuth spectrum expansion effect caused by the motion of bistatic platforms, the Doppler frequency rate term is compensated for each range cell. (3) To achieve clutter cancellation, the space-time decoupling matrix is introduced. The normalized Doppler frequency remains unchanged, and the clutter atoms on the airborne BiSAR space-time plane are linearly transformed into atomic positions with the same normalized spatial frequency. Then, the echo signals of dual channels are subtracted for effective clutter suppression. The effectiveness of the proposed method for airborne BiSAR clutter suppression is demonstrated through simulation and real data processing.
This study addresses the issue of fine-grained feature extraction and classification for Low-Slow-Small (LSS) targets, such as birds and drones, by proposing a multi-band multi-angle feature fusion classification method. First, data from five types of rotorcraft drones and bird models were collected at multiple angles using K-band and L-band frequency-modulated continuous-wave radars, forming a dataset for LSS target detection. Second, to capture the periodic vibration characteristics of the L-band target signals, empirical mode decomposition was applied to extract high-frequency features and reduce noise interference. For the K-band echo signals, short-time Fourier transform was applied to obtain high-resolution micro-Doppler features from various angles. Based on these features, a Multi-band Multi-angle Feature Fusion Network (MMFFNet) was designed, incorporating an improved convolutional long short-term memory network for temporal feature extraction, along with an attention fusion module and a multiscale feature fusion module. The proposed architecture improves target classification accuracy by integrating features from both bands and angles. Validation using a real-world dataset showed that compared with methods relying on single radar features, the proposed approach improved the classification accuracy for seven types of LSS targets by 3.1% under a high Signal-to-Noise Ratio (SNR) of 5 dB and by 12.3% under a low SNR of −3 dB.
This study addresses the issue of fine-grained feature extraction and classification for Low-Slow-Small (LSS) targets, such as birds and drones, by proposing a multi-band multi-angle feature fusion classification method. First, data from five types of rotorcraft drones and bird models were collected at multiple angles using K-band and L-band frequency-modulated continuous-wave radars, forming a dataset for LSS target detection. Second, to capture the periodic vibration characteristics of the L-band target signals, empirical mode decomposition was applied to extract high-frequency features and reduce noise interference. For the K-band echo signals, short-time Fourier transform was applied to obtain high-resolution micro-Doppler features from various angles. Based on these features, a Multi-band Multi-angle Feature Fusion Network (MMFFNet) was designed, incorporating an improved convolutional long short-term memory network for temporal feature extraction, along with an attention fusion module and a multiscale feature fusion module. The proposed architecture improves target classification accuracy by integrating features from both bands and angles. Validation using a real-world dataset showed that compared with methods relying on single radar features, the proposed approach improved the classification accuracy for seven types of LSS targets by 3.1% under a high Signal-to-Noise Ratio (SNR) of 5 dB and by 12.3% under a low SNR of −3 dB.
The widespread application of wireless communication devices in emerging scenarios (e.g., Vehicle-to-Everything, Low Earth Orbit Satellites) has gradually pushed communication frequencies toward higher bands, resulting in an increasingly prominent overlap with radar frequency bands. A Dual-Functional Radar-Communication (DFRC) system, with its joint signal processing capabilities and low-power characteristics, is regarded as effective in alleviating spectrum congestion. Unlike traditional antenna array architectures, Holographic Metasurface Antennas (HMAs) embed closely arranged metamaterial units, enabling the flexible configuration of each unit’s state to regulate frequency responses. This facilitates controllable and energy-efficient beamforming, offering potential for application in DFRC systems. Considering an HMA-based DFRC system that performs target sensing in a cluttered environment while providing communication services to multiple single-antenna users, this paper formulates an optimization problem to maximize the weighted sum of communication spectral efficiency and radar mutual information, subject to constraints on the transmission power and HMA frequency response. It jointly optimizes the involved digital precoder, HMA weight matrix, and receive filter to realize an HMA-based DFRC beamforming design. To tackle this nonconvex optimization challenge, we propose an alternating optimization algorithm based on fractional programming. This algorithm first employs fractional programming techniques to transform the original problem into more manageable subproblems, which are then alternately solved using methods such as Lagrangian dual decomposition and manifold optimization. Simulation results show that the beamforming design with the HMA array architecture achieves a flexible tradeoff between communication spectral efficiency and radar mutual information performance, approaching the performance of a fully digital array architecture.
The widespread application of wireless communication devices in emerging scenarios (e.g., Vehicle-to-Everything, Low Earth Orbit Satellites) has gradually pushed communication frequencies toward higher bands, resulting in an increasingly prominent overlap with radar frequency bands. A Dual-Functional Radar-Communication (DFRC) system, with its joint signal processing capabilities and low-power characteristics, is regarded as effective in alleviating spectrum congestion. Unlike traditional antenna array architectures, Holographic Metasurface Antennas (HMAs) embed closely arranged metamaterial units, enabling the flexible configuration of each unit’s state to regulate frequency responses. This facilitates controllable and energy-efficient beamforming, offering potential for application in DFRC systems. Considering an HMA-based DFRC system that performs target sensing in a cluttered environment while providing communication services to multiple single-antenna users, this paper formulates an optimization problem to maximize the weighted sum of communication spectral efficiency and radar mutual information, subject to constraints on the transmission power and HMA frequency response. It jointly optimizes the involved digital precoder, HMA weight matrix, and receive filter to realize an HMA-based DFRC beamforming design. To tackle this nonconvex optimization challenge, we propose an alternating optimization algorithm based on fractional programming. This algorithm first employs fractional programming techniques to transform the original problem into more manageable subproblems, which are then alternately solved using methods such as Lagrangian dual decomposition and manifold optimization. Simulation results show that the beamforming design with the HMA array architecture achieves a flexible tradeoff between communication spectral efficiency and radar mutual information performance, approaching the performance of a fully digital array architecture.
Covert Unmanned Aerial Vehicle (UAV) communication has garnered considerable attention for realizing a sustainable Low-Altitude Economy (LAE). Based on the Integrated Sensing And Communication (ISAC) framework, this paper studies the system strategies and resource allocation for a cooperative multi-UAV covert communication network, where multiple UAVs are employed to simultaneously conduct cooperative sensing and covert downlink transmissions to multiple Ground Users (GUs) in the presence of a mobile warden (Willie). To improve communication covertness, UAVs adaptively switch between Jamming Unmanned Aerial Vehicle (JUAV) mode and Information Unmanned Aerial Vehicle (IUAV) mode. To cope with the mobility of Willie, an Unscented Kalman Filtering (UKF)-based method is employed to track and predict Willie's location using delay and Doppler measurements extracted from ISAC echoes. By jointly optimizing the JUAV selection strategy, IUAV-GU scheduling, and communication/jamming power allocation, a real-time fairness transmission maximization problem is formulated. The Alternating Optimization (AO) approach is adopted to decompose the original problem into a series of sub-problems, resulting in an efficient sub-optimal solution. Simulation results demonstrate that the proposed scheme can accurately track Willie and effectively ensure covert downlink transmission.
Covert Unmanned Aerial Vehicle (UAV) communication has garnered considerable attention for realizing a sustainable Low-Altitude Economy (LAE). Based on the Integrated Sensing And Communication (ISAC) framework, this paper studies the system strategies and resource allocation for a cooperative multi-UAV covert communication network, where multiple UAVs are employed to simultaneously conduct cooperative sensing and covert downlink transmissions to multiple Ground Users (GUs) in the presence of a mobile warden (Willie). To improve communication covertness, UAVs adaptively switch between Jamming Unmanned Aerial Vehicle (JUAV) mode and Information Unmanned Aerial Vehicle (IUAV) mode. To cope with the mobility of Willie, an Unscented Kalman Filtering (UKF)-based method is employed to track and predict Willie's location using delay and Doppler measurements extracted from ISAC echoes. By jointly optimizing the JUAV selection strategy, IUAV-GU scheduling, and communication/jamming power allocation, a real-time fairness transmission maximization problem is formulated. The Alternating Optimization (AO) approach is adopted to decompose the original problem into a series of sub-problems, resulting in an efficient sub-optimal solution. Simulation results demonstrate that the proposed scheme can accurately track Willie and effectively ensure covert downlink transmission.
Joint radar communication leverages resource-sharing mechanisms to improve system spectrum utilization and achieve lightweight design. It has wide applications in air traffic control, healthcare monitoring, and autonomous vehicles. Traditional joint radar communication algorithms often rely on precise mathematical modeling and channel estimation and cannot adapt to dynamic and complex environments that are difficult to describe. Artificial Intelligence (AI), with its powerful learning ability, automatically learns features from large amounts of data without the need for explicit modeling, thereby promoting the deep fusion of radar communication. This article provides a systematic review of the research on AI-driven joint radar communication. Specifically, the model and challenges of the joint radar communication system are first elaborated. On this basis, the latest research progress on AI-driven joint radar communication is summarized from two aspects: radar communication coexistence and dual-functional radar communication. Finally, the article is summarized, and the potential technical challenges and future research directions in this field are described.
Joint radar communication leverages resource-sharing mechanisms to improve system spectrum utilization and achieve lightweight design. It has wide applications in air traffic control, healthcare monitoring, and autonomous vehicles. Traditional joint radar communication algorithms often rely on precise mathematical modeling and channel estimation and cannot adapt to dynamic and complex environments that are difficult to describe. Artificial Intelligence (AI), with its powerful learning ability, automatically learns features from large amounts of data without the need for explicit modeling, thereby promoting the deep fusion of radar communication. This article provides a systematic review of the research on AI-driven joint radar communication. Specifically, the model and challenges of the joint radar communication system are first elaborated. On this basis, the latest research progress on AI-driven joint radar communication is summarized from two aspects: radar communication coexistence and dual-functional radar communication. Finally, the article is summarized, and the potential technical challenges and future research directions in this field are described.
Because the Terahertz (THz) band is capable of achieving terabit-per-second communication rates and high-precision sensing, THz Integrated Sensing And Communication (ISAC) has become a key technology for future wireless systems. We propose a THz ISAC framework based on a delay-Doppler waveform, i.e., the Orthogonal Delay-Doppler Multiplexing (ODDM) modulation. A more general off-grid ODDM modulation input/output relationship is derived to eliminate the assumption that channel path delays and Doppler frequency shifts are integer multiples of their resolutions. For ODDM symbol detection, a time-domain channel equalizer based on the conjugate gradient method is proposed to optimize the computational complexity. Compared with orthogonal frequency division multiplexing, ODDM demonstrates higher Doppler robustness against the Doppler effect. A sensing estimation algorithm is designed to achieve high-precision estimates with low complexity. The results show that the multi-target estimation accuracy approaches Cramér-Rao lower bounds.
Because the Terahertz (THz) band is capable of achieving terabit-per-second communication rates and high-precision sensing, THz Integrated Sensing And Communication (ISAC) has become a key technology for future wireless systems. We propose a THz ISAC framework based on a delay-Doppler waveform, i.e., the Orthogonal Delay-Doppler Multiplexing (ODDM) modulation. A more general off-grid ODDM modulation input/output relationship is derived to eliminate the assumption that channel path delays and Doppler frequency shifts are integer multiples of their resolutions. For ODDM symbol detection, a time-domain channel equalizer based on the conjugate gradient method is proposed to optimize the computational complexity. Compared with orthogonal frequency division multiplexing, ODDM demonstrates higher Doppler robustness against the Doppler effect. A sensing estimation algorithm is designed to achieve high-precision estimates with low complexity. The results show that the multi-target estimation accuracy approaches Cramér-Rao lower bounds.
This study explores the use of one-bit Digital-to-Analog Converters (DAC) to mitigate the challenges of high hardware costs and excessive power consumption in large-scale Multiple-Input Multiple-Output (MIMO) communication and radar systems. The present study focuses on the design of one-bit transmit waveforms for dual-functional radar and communication systems. Under preset communication Quality of Service (QoS) constraints, the objective was to minimize the integral sidelobe-to-mainlobe ratio of the radar transmit beampattern. This should help enhance the power concentration of the transmitted beampattern and improve the performance of the beampattern synthesis. To address the limited Degrees of Freedom (DoF) caused by one-bit quantization, this study employs symbol-level precoding technology and then fully utilizes the DoFs in spatial and temporal domains to assist waveform design based on the principle of Constructive Interference (CI). To address the nonconvex fractional quadratic objective function and the multiple nonconvex discrete constraints inherent in the proposed waveform design problem, this study introduces an algorithm that combines the Dinkelbach transform with the Alternating Direction Method of Multipliers (ADMM). This approach effectively tackles the NP-hard problem. The numerical results demonstrate that the designed waveform significantly reduces the required DAC resolution and achieves excellent radar beampattern performance while satisfying the QoS requirements of downlink multiuser communications.
This study explores the use of one-bit Digital-to-Analog Converters (DAC) to mitigate the challenges of high hardware costs and excessive power consumption in large-scale Multiple-Input Multiple-Output (MIMO) communication and radar systems. The present study focuses on the design of one-bit transmit waveforms for dual-functional radar and communication systems. Under preset communication Quality of Service (QoS) constraints, the objective was to minimize the integral sidelobe-to-mainlobe ratio of the radar transmit beampattern. This should help enhance the power concentration of the transmitted beampattern and improve the performance of the beampattern synthesis. To address the limited Degrees of Freedom (DoF) caused by one-bit quantization, this study employs symbol-level precoding technology and then fully utilizes the DoFs in spatial and temporal domains to assist waveform design based on the principle of Constructive Interference (CI). To address the nonconvex fractional quadratic objective function and the multiple nonconvex discrete constraints inherent in the proposed waveform design problem, this study introduces an algorithm that combines the Dinkelbach transform with the Alternating Direction Method of Multipliers (ADMM). This approach effectively tackles the NP-hard problem. The numerical results demonstrate that the designed waveform significantly reduces the required DAC resolution and achieves excellent radar beampattern performance while satisfying the QoS requirements of downlink multiuser communications.
With the widespread application of Wi-Fi sensing technology in intelligent health monitoring, constructing high-quality perception datasets has become a key challenge. Particularly in monitoring abnormal behaviors, such as falls, traditional methods rely on repeated human experiments, which not only poses safety risks but also raises ethical concerns. To address these issues, this paper proposes a time-domain digital coding metasurface-assisted data acquisition method. By simulating the Doppler effect and micro-Doppler characteristics of the human body, the time-domain digital coding metasurface can effectively replace human experiments and assist in constructing Wi-Fi sensing datasets. To verify the feasibility of this method, we develop a time-domain digital coding metasurface with 0°–360° full-phase modulation capability. Experimental results show that the signals generated by the metasurface retain the motion characteristics of the human body, complement real samples, reduce the complexity of data collection, and finally improve the monitoring accuracy of the classification model significantly. This method provides an innovative and feasible solution for data acquisition for Wi-Fi sensing technology.
With the widespread application of Wi-Fi sensing technology in intelligent health monitoring, constructing high-quality perception datasets has become a key challenge. Particularly in monitoring abnormal behaviors, such as falls, traditional methods rely on repeated human experiments, which not only poses safety risks but also raises ethical concerns. To address these issues, this paper proposes a time-domain digital coding metasurface-assisted data acquisition method. By simulating the Doppler effect and micro-Doppler characteristics of the human body, the time-domain digital coding metasurface can effectively replace human experiments and assist in constructing Wi-Fi sensing datasets. To verify the feasibility of this method, we develop a time-domain digital coding metasurface with 0°–360° full-phase modulation capability. Experimental results show that the signals generated by the metasurface retain the motion characteristics of the human body, complement real samples, reduce the complexity of data collection, and finally improve the monitoring accuracy of the classification model significantly. This method provides an innovative and feasible solution for data acquisition for Wi-Fi sensing technology.
Beamforming enhances the received signal power by transmitting signals in specific directions. However, in high-speed and dynamic vehicular network scenarios, frequent channel state updates and beam adjustments impose substantial system overhead. Furthermore, real-time alignment between the beam and user location becomes challenging, leading to potential misalignment that undermines communication stability. Obstructions and channel fading in complex road environments further constrain the effectiveness of beamforming. To address these challenges, this study proposes a multimodal feature fusion beamforming method based on a convolutional neural network and an attention mechanism model to achieve sensor-assisted high-reliability communication. Data heterogeneity is solved by customizing data conversion and standardization strategies for radar and lidar data collected by sensors. Three-dimensional convolutional residual blocks are employed to extract multimodal features, while the cross-attention mechanism integrates integrate these features for beamforming. Experimental results show that the proposed method achieves an average Top-3 accuracy of nearly 90% in high-speed environments, which is substantially improved compared with the single-modal beamforming scheme.
Beamforming enhances the received signal power by transmitting signals in specific directions. However, in high-speed and dynamic vehicular network scenarios, frequent channel state updates and beam adjustments impose substantial system overhead. Furthermore, real-time alignment between the beam and user location becomes challenging, leading to potential misalignment that undermines communication stability. Obstructions and channel fading in complex road environments further constrain the effectiveness of beamforming. To address these challenges, this study proposes a multimodal feature fusion beamforming method based on a convolutional neural network and an attention mechanism model to achieve sensor-assisted high-reliability communication. Data heterogeneity is solved by customizing data conversion and standardization strategies for radar and lidar data collected by sensors. Three-dimensional convolutional residual blocks are employed to extract multimodal features, while the cross-attention mechanism integrates integrate these features for beamforming. Experimental results show that the proposed method achieves an average Top-3 accuracy of nearly 90% in high-speed environments, which is substantially improved compared with the single-modal beamforming scheme.
In recent years, target recognition systems based on radar sensor networks have been widely studied in the field of automatic target recognition. These systems observe the target from multiple angles to achieve robust recognition, which also brings the problem of using the correlation and difference information of multiradar sensor echo data. Furthermore, most existing studies used large-scale labeled data to obtain prior knowledge of the target. Considering that a large amount of unlabeled data is not effectively used in target recognition tasks, this paper proposes an HRRP unsupervised target feature extraction method based on Multiple Contrastive Loss (MCL) in radar sensor networks. The proposed method combines instance level loss, Fisher loss, and semantic consistency loss constraints to identify consistent and discriminative feature vectors among the echoes of multiple radar sensors and then use them in subsequent target recognition tasks. Specifically, the original echo data are mapped to the contrast loss space and the semantic label space. In the contrast loss space, the contrastive loss is used to constrain the similarity and aggregation of samples so that the relative and absolute distances between different echoes of the same target obtained by different sensors are reduced while the relative and absolute distances between different target echoes are increased. In the semantic loss space, the extracted discriminant features are used to constrain the semantic labels so that the semantic information and discriminant features are consistent. Experiments on an actual civil aircraft dataset revealed that the target recognition accuracy of the MCL-based method is improved by 0.4% and 1.4%, respectively, compared with the most advanced unsupervised algorithm CC and supervised target recognition algorithm PNN. Further, MCL can effectively improve the target recognition performance of radar sensors when applied in conjunction with the sensors.
In recent years, target recognition systems based on radar sensor networks have been widely studied in the field of automatic target recognition. These systems observe the target from multiple angles to achieve robust recognition, which also brings the problem of using the correlation and difference information of multiradar sensor echo data. Furthermore, most existing studies used large-scale labeled data to obtain prior knowledge of the target. Considering that a large amount of unlabeled data is not effectively used in target recognition tasks, this paper proposes an HRRP unsupervised target feature extraction method based on Multiple Contrastive Loss (MCL) in radar sensor networks. The proposed method combines instance level loss, Fisher loss, and semantic consistency loss constraints to identify consistent and discriminative feature vectors among the echoes of multiple radar sensors and then use them in subsequent target recognition tasks. Specifically, the original echo data are mapped to the contrast loss space and the semantic label space. In the contrast loss space, the contrastive loss is used to constrain the similarity and aggregation of samples so that the relative and absolute distances between different echoes of the same target obtained by different sensors are reduced while the relative and absolute distances between different target echoes are increased. In the semantic loss space, the extracted discriminant features are used to constrain the semantic labels so that the semantic information and discriminant features are consistent. Experiments on an actual civil aircraft dataset revealed that the target recognition accuracy of the MCL-based method is improved by 0.4% and 1.4%, respectively, compared with the most advanced unsupervised algorithm CC and supervised target recognition algorithm PNN. Further, MCL can effectively improve the target recognition performance of radar sensors when applied in conjunction with the sensors.
Integrated Sensing And Communications (ISAC) based on reusing random communication signals within the existing network architecture may drastically reduce implementation costs, thereby accelerating the integration of sensing functionalities into current communication networks. However, the randomness of communication data introduces fluctuations in sensing performance across different signal realizations, leading to unstable sensing accuracy. To address this issue, we delve into random ISAC signal processing methods and propose a joint transceiver precoding optimization design for Multiple-Input Multiple-Output ISAC (MIMO-ISAC) systems. Specifically, considering target impulse response matrix estimation, we first define the Ergodic Cramér-Rao Bound (ECRB) as an average sensing performance metric under random signaling. By deriving the closed-form expression of the ECRB based on the distribution of complex inverse Wishart matrices, we theoretically reveal the performance loss arising when using random signals for sensing compared to the conventional deterministic orthogonal signals. Furthermore, we formulate the sensing-optimal subproblem by minimizing the ECRB and the communication-optimal subproblem of multiantenna multiuser signal estimation and derive the corresponding sensing-optimal and communication-optimal precoding designs. Subsequently, we extend the proposed transceiver precoding optimization framework to ISAC scenarios by explicitly constraining the communication requirements. Finally, through numerous simulations, we validate the effectiveness of the proposed method. The results demonstrate that the joint transceiver precoding design may allow high-accuracy target response matrix estimation while enabling flexible trade-offs between communication signal estimation and target response matrix estimation errors.
Integrated Sensing And Communications (ISAC) based on reusing random communication signals within the existing network architecture may drastically reduce implementation costs, thereby accelerating the integration of sensing functionalities into current communication networks. However, the randomness of communication data introduces fluctuations in sensing performance across different signal realizations, leading to unstable sensing accuracy. To address this issue, we delve into random ISAC signal processing methods and propose a joint transceiver precoding optimization design for Multiple-Input Multiple-Output ISAC (MIMO-ISAC) systems. Specifically, considering target impulse response matrix estimation, we first define the Ergodic Cramér-Rao Bound (ECRB) as an average sensing performance metric under random signaling. By deriving the closed-form expression of the ECRB based on the distribution of complex inverse Wishart matrices, we theoretically reveal the performance loss arising when using random signals for sensing compared to the conventional deterministic orthogonal signals. Furthermore, we formulate the sensing-optimal subproblem by minimizing the ECRB and the communication-optimal subproblem of multiantenna multiuser signal estimation and derive the corresponding sensing-optimal and communication-optimal precoding designs. Subsequently, we extend the proposed transceiver precoding optimization framework to ISAC scenarios by explicitly constraining the communication requirements. Finally, through numerous simulations, we validate the effectiveness of the proposed method. The results demonstrate that the joint transceiver precoding design may allow high-accuracy target response matrix estimation while enabling flexible trade-offs between communication signal estimation and target response matrix estimation errors.
Compared to ground-based external radiation source radar, satellite signal-based external radiation source radar (i.e., satellite signal external radiation source radar) offers advantages such as global, all-time, and all-weather coverage, which can compensate for the limitations of ground-based external radiation source radar in terms of maritime coverage. In contrast to medium and high-altitude satellite signals, Low-Earth Orbit (LEO) communication satellite signals have advantages such as strong reception power and a large number of satellites, which can provide substantial detection range and accuracy for passive detection of maritime targets. In response to future development needs, this paper provides a detailed discussion of the research status and application prospects of satellite signal external radiation source radar, and presents a feasibility analysis for constructing a low-earth orbit communication satellite signal external radiation source radar system using Iridium and Starlink, two types of LEO communication satellite systems, which integrates high and low frequencies with both wide and narrow bandwidths. Based on this, the paper summarizes the technical challenges and potential solutions in the development of low-earth orbit communication satellite signal external radiation source radar systems. The aforementioned research can serve as an important reference for wide-area external radiation source radar detection.
Compared to ground-based external radiation source radar, satellite signal-based external radiation source radar (i.e., satellite signal external radiation source radar) offers advantages such as global, all-time, and all-weather coverage, which can compensate for the limitations of ground-based external radiation source radar in terms of maritime coverage. In contrast to medium and high-altitude satellite signals, Low-Earth Orbit (LEO) communication satellite signals have advantages such as strong reception power and a large number of satellites, which can provide substantial detection range and accuracy for passive detection of maritime targets. In response to future development needs, this paper provides a detailed discussion of the research status and application prospects of satellite signal external radiation source radar, and presents a feasibility analysis for constructing a low-earth orbit communication satellite signal external radiation source radar system using Iridium and Starlink, two types of LEO communication satellite systems, which integrates high and low frequencies with both wide and narrow bandwidths. Based on this, the paper summarizes the technical challenges and potential solutions in the development of low-earth orbit communication satellite signal external radiation source radar systems. The aforementioned research can serve as an important reference for wide-area external radiation source radar detection.
Dual Function Radar and Communication (DFRC)-integrated electronic equipment platform, which combines detection and communication functions, effectively addresses issues such as platform limitations, resource constraints, and electromagnetic compatibility by sharing hardware platforms and transmitting waveforms. Therefore, it has become a research hotspot in recent years. The DFRC technology, centered on detection functionality and incorporating limited communication capabilities, has remarkable application prospects in typical detection scenarios, such as early warning and surveillance and tracking guidance under future combat conditions. This paper focuses on using the signal design method to optimize radar detection performance by effectively adjusting the trade-off between detection and communication in multi-domain resource utilization by guaranteeing a minimum communication performance. First, the performance measurement criteria of DFRC systems were summarized. Then, the paper provides a comprehensive introduction to the DFRC signal design methods under typical detection scenarios and a thorough analysis of the problems and current solutions of each signal design method. Finally, a summary and future research directions are outlined.
Dual Function Radar and Communication (DFRC)-integrated electronic equipment platform, which combines detection and communication functions, effectively addresses issues such as platform limitations, resource constraints, and electromagnetic compatibility by sharing hardware platforms and transmitting waveforms. Therefore, it has become a research hotspot in recent years. The DFRC technology, centered on detection functionality and incorporating limited communication capabilities, has remarkable application prospects in typical detection scenarios, such as early warning and surveillance and tracking guidance under future combat conditions. This paper focuses on using the signal design method to optimize radar detection performance by effectively adjusting the trade-off between detection and communication in multi-domain resource utilization by guaranteeing a minimum communication performance. First, the performance measurement criteria of DFRC systems were summarized. Then, the paper provides a comprehensive introduction to the DFRC signal design methods under typical detection scenarios and a thorough analysis of the problems and current solutions of each signal design method. Finally, a summary and future research directions are outlined.
Bistatic Synthetic Aperture Radar (SAR), with the separated transmitter and receiver working in coordination, cannot only achieves high-resolution imaging in the forward-looking mode, but also possesses outstanding concealment and anti-interference capabilities. Therefore, bistatic SAR thrives in both civilian and military applications, such as ocean monitoring or reconnaissance imaging. However, ship targets are typically influenced by sea waves, generating unknown and complex three-dimensional oscillations. These random oscillations and radar motions vary with slow time, making the imaging view of bistatic SAR ship targets strongly time-dependent, so that it is extremely difficult to extract effective target features from final imaging results. Moreover, target oscillations are also coupled with the motion of bistatic platforms, which causes severe nonlinear spatial Doppler shifts in target echoes, and thus bistatic SAR images are usually defocused. To address these problems, this paper proposes an imaging method for bistatic SAR ship target by imaging time optimization, which generates well-focused bistatic SAR ship target images with the optimal views. Firstly, short-time Fourier transform is utilized to extract the time-frequency information of the ship. Secondly, based on this time-frequency information from multiple strong scatterers, the optimal three-dimensional rotation parameters are estimated, revealing the time-varying characteristics of the imaging projection plane. Then, the optimal imaging time centers are selected based on the optimal imaging projection planes, while the corresponding optimal imaging time intervals are chosen based on the optimal imaging resolutions. Finally, with the selected optimal imaging times, the desired images of the bistatic SAR ship target are produced. Simulation experiments verify the accuracy of target rotation parameter estimation under different bistatic configurations and noise conditions, as well as the effectiveness of imaging projection plane selection. In general, this method tackles with the issues of the time-varying imaging views of bistatic SAR ship targets and nonlinear spatial Doppler shifts, obtaining well-focused and optimally viewed target images, which significantly enhances the accuracy of subsequent target feature extraction.
Bistatic Synthetic Aperture Radar (SAR), with the separated transmitter and receiver working in coordination, cannot only achieves high-resolution imaging in the forward-looking mode, but also possesses outstanding concealment and anti-interference capabilities. Therefore, bistatic SAR thrives in both civilian and military applications, such as ocean monitoring or reconnaissance imaging. However, ship targets are typically influenced by sea waves, generating unknown and complex three-dimensional oscillations. These random oscillations and radar motions vary with slow time, making the imaging view of bistatic SAR ship targets strongly time-dependent, so that it is extremely difficult to extract effective target features from final imaging results. Moreover, target oscillations are also coupled with the motion of bistatic platforms, which causes severe nonlinear spatial Doppler shifts in target echoes, and thus bistatic SAR images are usually defocused. To address these problems, this paper proposes an imaging method for bistatic SAR ship target by imaging time optimization, which generates well-focused bistatic SAR ship target images with the optimal views. Firstly, short-time Fourier transform is utilized to extract the time-frequency information of the ship. Secondly, based on this time-frequency information from multiple strong scatterers, the optimal three-dimensional rotation parameters are estimated, revealing the time-varying characteristics of the imaging projection plane. Then, the optimal imaging time centers are selected based on the optimal imaging projection planes, while the corresponding optimal imaging time intervals are chosen based on the optimal imaging resolutions. Finally, with the selected optimal imaging times, the desired images of the bistatic SAR ship target are produced. Simulation experiments verify the accuracy of target rotation parameter estimation under different bistatic configurations and noise conditions, as well as the effectiveness of imaging projection plane selection. In general, this method tackles with the issues of the time-varying imaging views of bistatic SAR ship targets and nonlinear spatial Doppler shifts, obtaining well-focused and optimally viewed target images, which significantly enhances the accuracy of subsequent target feature extraction.
With the emergence of the low-altitude economy, the communication and detection issues of Unmanned Aerial Vehicles (UAVs) have gained considerable attention. This paper investigates sensing reference signal design for Integrated Sensing And Communication (ISAC) in Orthogonal Frequency Division Multiplexing (OFDM) systems aimed at detecting long-range, high-speed UAVs. To address the ambiguity problem in long-range and high-speed UAV detection, traditional reference signal designs require densely arranged reference signals, leading to significant resource overhead. In addition, long-range detection based on OFDM waveforms faces challenges from Inter-Symbol Interference (ISI). To address these issues, this paper first proposes a reference signal pattern that supports long-range detection and resists ISI, achieving the maximum unambiguous detection range of the system with reduced resource overhead. Then, to address the challenge of high-speed detection, the paper incorporates range-rate into the Chinese Remainder Theorem-based method. Through the proper configuration of sensing reference signals and the cancellation of ghost targets, this approach significantly increases the unambiguous detection velocity while minimizing resource usage and avoiding the generation of ghost targets. The effectiveness of the proposed methods is validated through simulations. Simulation results show that compared with the traditional sensing reference signal design, our proposed scheme can reduce 72% overhead of reference signals for long-range and high-speed UAV detections.
With the emergence of the low-altitude economy, the communication and detection issues of Unmanned Aerial Vehicles (UAVs) have gained considerable attention. This paper investigates sensing reference signal design for Integrated Sensing And Communication (ISAC) in Orthogonal Frequency Division Multiplexing (OFDM) systems aimed at detecting long-range, high-speed UAVs. To address the ambiguity problem in long-range and high-speed UAV detection, traditional reference signal designs require densely arranged reference signals, leading to significant resource overhead. In addition, long-range detection based on OFDM waveforms faces challenges from Inter-Symbol Interference (ISI). To address these issues, this paper first proposes a reference signal pattern that supports long-range detection and resists ISI, achieving the maximum unambiguous detection range of the system with reduced resource overhead. Then, to address the challenge of high-speed detection, the paper incorporates range-rate into the Chinese Remainder Theorem-based method. Through the proper configuration of sensing reference signals and the cancellation of ghost targets, this approach significantly increases the unambiguous detection velocity while minimizing resource usage and avoiding the generation of ghost targets. The effectiveness of the proposed methods is validated through simulations. Simulation results show that compared with the traditional sensing reference signal design, our proposed scheme can reduce 72% overhead of reference signals for long-range and high-speed UAV detections.
This paper proposes an intelligent framework based on a cell-free network architecture, called HRT-Net. HRT-Net is designed to enhance multi-station collaborative sensing problems for joint radar and communication systems, offering accurate and resource-efficient target location estimation. First, the sensing area is divided into sub-regions and a lightweight region selection network employing depthwise separable convolution; this approach coarsely identifies the target’s sub-region, reducing computational demands and enabling extensive area coverage. To tackle interstation data disparity, we propose a channel-wise unidimensional attention mechanism. This mechanism aggregates multi-station sensing data effectively, enhancing feature extraction and representation by generating attention weight maps that refine the original features. Finally, we design a target localization network featuring multi-scale and multi-residual connections. This network extracts comprehensive, deep features and achieves multi-level feature fusion, allowing for reliable mapping of data to the target coordinates. Extensive simulations and real-world experiments validate the effectiveness and robustness of our scheme. The results show that compared with the existing methods, HRT-Net achieves centimeter-level target localization with low computational complexity and minimal storage overhead.
This paper proposes an intelligent framework based on a cell-free network architecture, called HRT-Net. HRT-Net is designed to enhance multi-station collaborative sensing problems for joint radar and communication systems, offering accurate and resource-efficient target location estimation. First, the sensing area is divided into sub-regions and a lightweight region selection network employing depthwise separable convolution; this approach coarsely identifies the target’s sub-region, reducing computational demands and enabling extensive area coverage. To tackle interstation data disparity, we propose a channel-wise unidimensional attention mechanism. This mechanism aggregates multi-station sensing data effectively, enhancing feature extraction and representation by generating attention weight maps that refine the original features. Finally, we design a target localization network featuring multi-scale and multi-residual connections. This network extracts comprehensive, deep features and achieves multi-level feature fusion, allowing for reliable mapping of data to the target coordinates. Extensive simulations and real-world experiments validate the effectiveness and robustness of our scheme. The results show that compared with the existing methods, HRT-Net achieves centimeter-level target localization with low computational complexity and minimal storage overhead.
This paper addresses the task allocation problem in swarm Unmanned Aerial Vehicle (UAV) Synthetic Aperture Radar (SAR) systems and proposes a method based on low-redundancy chromosome encoding. It starts with a thorough analysis of the relationship between imaging performance and geometric configurations in SAR imaging tasks and accordingly constructs a path function that reflects imaging resolution performance. The task allocation problem is then formulated as a generalized, balanced multiple traveling salesman problem. To enhance the search efficiency and accuracy of the algorithm, a two-part chromosome encoding scheme with low redundancy is introduced. Additionally, considering possible unexpected situations and dynamic changes in practical applications, a dynamic task allocation strategy integrating a contract net protocol and attention mechanisms is proposed. This method can flexibly adjust task allocation strategies based on actual conditions, ensuring the robustness of the system. Simulation experiments validate the effectiveness of the proposed method.
This paper addresses the task allocation problem in swarm Unmanned Aerial Vehicle (UAV) Synthetic Aperture Radar (SAR) systems and proposes a method based on low-redundancy chromosome encoding. It starts with a thorough analysis of the relationship between imaging performance and geometric configurations in SAR imaging tasks and accordingly constructs a path function that reflects imaging resolution performance. The task allocation problem is then formulated as a generalized, balanced multiple traveling salesman problem. To enhance the search efficiency and accuracy of the algorithm, a two-part chromosome encoding scheme with low redundancy is introduced. Additionally, considering possible unexpected situations and dynamic changes in practical applications, a dynamic task allocation strategy integrating a contract net protocol and attention mechanisms is proposed. This method can flexibly adjust task allocation strategies based on actual conditions, ensuring the robustness of the system. Simulation experiments validate the effectiveness of the proposed method.
The miniature multistatic Synthetic Aperture Radar (SAR) system uses a flexible configuration of transceiver division compared with the miniature monostatic SAR system, thereby affording the advantages of multi-angle imaging. As the transceiver-separated SAR system uses mutually independent oscillator sources, phase synchronization is necessary for high-precision imaging of the miniature multistatic SAR. Although current research on phase synchronization schemes for bistatic SAR is relatively mature, these schemes are primarily based on the pulse SAR system. However, a paucity of research exists on phase synchronization for the miniature multistatic Frequency Modulated Continuous Wave (FMCW) SAR. In comparison with the pulse SAR, the FMCW SAR system lacks a temporal interval between the transmitted pulses. Consequently, some phase synchronization schemes developed for the pulse SAR system cannot be directly applied to the FMCW SAR system. To this end, this study proposes a novel phase synchronization method for the miniature multistatic FMCW SAR, effectively resolving the problem of the FMCW SAR. This method uses the generalized Short-Time Shift-Orthogonal (STSO) waveform as the phase synchronization signal of disparate radar platforms. The phase error between the radar platforms can be effectively extracted through pulse compression to realize phase synchronization. Compared with the conventional linear frequency-modulated waveform, after the generalized STSO waveform is pulsed by the same pulse compression function, the interference signal energy is concentrated away from the peak of the matching signal and the phase synchronization accuracy is enhanced. Furthermore, the proposed method is adapted to the characteristics of dechirp reception in FMCW miniature multistatic SAR systems, and ground and numerical simulation experiments verify that the proposed method has high synchronization accuracy.
The miniature multistatic Synthetic Aperture Radar (SAR) system uses a flexible configuration of transceiver division compared with the miniature monostatic SAR system, thereby affording the advantages of multi-angle imaging. As the transceiver-separated SAR system uses mutually independent oscillator sources, phase synchronization is necessary for high-precision imaging of the miniature multistatic SAR. Although current research on phase synchronization schemes for bistatic SAR is relatively mature, these schemes are primarily based on the pulse SAR system. However, a paucity of research exists on phase synchronization for the miniature multistatic Frequency Modulated Continuous Wave (FMCW) SAR. In comparison with the pulse SAR, the FMCW SAR system lacks a temporal interval between the transmitted pulses. Consequently, some phase synchronization schemes developed for the pulse SAR system cannot be directly applied to the FMCW SAR system. To this end, this study proposes a novel phase synchronization method for the miniature multistatic FMCW SAR, effectively resolving the problem of the FMCW SAR. This method uses the generalized Short-Time Shift-Orthogonal (STSO) waveform as the phase synchronization signal of disparate radar platforms. The phase error between the radar platforms can be effectively extracted through pulse compression to realize phase synchronization. Compared with the conventional linear frequency-modulated waveform, after the generalized STSO waveform is pulsed by the same pulse compression function, the interference signal energy is concentrated away from the peak of the matching signal and the phase synchronization accuracy is enhanced. Furthermore, the proposed method is adapted to the characteristics of dechirp reception in FMCW miniature multistatic SAR systems, and ground and numerical simulation experiments verify that the proposed method has high synchronization accuracy.
The ionosphere can distort received signals, degrade imaging quality, and decrease interferometric and polarimetric accuracies of spaceborne Synthetic Aperture Radars (SAR). The low-frequency systems operating at L-band and P-band are very susceptible to such problems. From another viewpoint, low-frequency spaceborne SARs can capture ionospheric structures with different spatial scales over the observed scope, and their echo and image data have sufficient ionospheric information, offering great probability for high-precision and high-resolution ionospheric probing. The research progress of ionospheric probing based on spaceborne SARs is reviewed in this paper. The technological system of this field is summarized from three aspects: Mapping of background ionospheric total electron content, tomography of ionospheric electron density, and probing of ionospheric irregularities. The potential of the low-frequency spaceborne SARs in mapping ionospheric local refined structures and global tendency is emphasized, and the future development direction is prospected.
The ionosphere can distort received signals, degrade imaging quality, and decrease interferometric and polarimetric accuracies of spaceborne Synthetic Aperture Radars (SAR). The low-frequency systems operating at L-band and P-band are very susceptible to such problems. From another viewpoint, low-frequency spaceborne SARs can capture ionospheric structures with different spatial scales over the observed scope, and their echo and image data have sufficient ionospheric information, offering great probability for high-precision and high-resolution ionospheric probing. The research progress of ionospheric probing based on spaceborne SARs is reviewed in this paper. The technological system of this field is summarized from three aspects: Mapping of background ionospheric total electron content, tomography of ionospheric electron density, and probing of ionospheric irregularities. The potential of the low-frequency spaceborne SARs in mapping ionospheric local refined structures and global tendency is emphasized, and the future development direction is prospected.
Bistatic Synthetic Aperture Radar (BiSAR) needs to suppress ground background clutter when detecting and imaging ground moving targets. However, due to the spatial configuration of BiSAR, the clutter poses a serious space-time nonstationary problem, which deteriorates the clutter suppression performance. Although Space-Time Adaptive Processing based on Sparse Recovery (SR-STAP) can reduce the nonstationary problem by reducing the number of samples, the off-grid dictionary problem will occur during processing, resulting in a decrease in the space-time spectrum estimation effect. Although most of the typical SR-STAP methods have clear mathematical relations and interpretability, they also have some problems, such as improper parameter setting and complicated operation in complex and changeable scenes. To solve the aforementioned problems, a complex neural network based on the Alternating Direction Multiplier Method (ADMM), is proposed for BiSAR space-time adaptive clutter suppression. First, a sparse recovery model of the continuous clutter space-time domain of BiSAR is constructed based on the Atomic Norm Minimization (ANM) to overcome the off-grid problem associated with the traditional discrete dictionary model. Second, ADMM is used to rapidly and iteratively solve the BiSAR clutter spectral sparse recovery model. Third according to the iterative and data flow diagrams, the artificial hyperparameter iterative process is transformed into ANM-ADMM-Net. Then, the normalized root-mean-square-error network loss function is set up and the network model is trained with the obtained data set. Finally, the trained ANM-ADMM-Net architecture is used to quickly process BiSAR echo data, and the space-time spectrum of BiSAR clutter is accurately estimated and efficiently restrained. The effectiveness of this approach is validated through simulations and airborne BiSAR clutter suppression experiments.
Bistatic Synthetic Aperture Radar (BiSAR) needs to suppress ground background clutter when detecting and imaging ground moving targets. However, due to the spatial configuration of BiSAR, the clutter poses a serious space-time nonstationary problem, which deteriorates the clutter suppression performance. Although Space-Time Adaptive Processing based on Sparse Recovery (SR-STAP) can reduce the nonstationary problem by reducing the number of samples, the off-grid dictionary problem will occur during processing, resulting in a decrease in the space-time spectrum estimation effect. Although most of the typical SR-STAP methods have clear mathematical relations and interpretability, they also have some problems, such as improper parameter setting and complicated operation in complex and changeable scenes. To solve the aforementioned problems, a complex neural network based on the Alternating Direction Multiplier Method (ADMM), is proposed for BiSAR space-time adaptive clutter suppression. First, a sparse recovery model of the continuous clutter space-time domain of BiSAR is constructed based on the Atomic Norm Minimization (ANM) to overcome the off-grid problem associated with the traditional discrete dictionary model. Second, ADMM is used to rapidly and iteratively solve the BiSAR clutter spectral sparse recovery model. Third according to the iterative and data flow diagrams, the artificial hyperparameter iterative process is transformed into ANM-ADMM-Net. Then, the normalized root-mean-square-error network loss function is set up and the network model is trained with the obtained data set. Finally, the trained ANM-ADMM-Net architecture is used to quickly process BiSAR echo data, and the space-time spectrum of BiSAR clutter is accurately estimated and efficiently restrained. The effectiveness of this approach is validated through simulations and airborne BiSAR clutter suppression experiments.
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2025, 14(3): 501-527.
With the expansion of China’s space interests and the growth in the scale of on-orbit assets, high-precision detection of dark and weak targets in noncooperative space has become the core bottleneck in space security defense and debris removal. Traditional optical or radar detection technologies are limited by diffraction limit and signal-to-noise ratio constraints, and the detection and identification accuracy of “fast, far, small, and dark” targets is insufficient. Light Detection and Ranging (LiDAR), with its high precision and anti-jamming advantages, has gradually become the core technical means of accurately detecting space targets. Technologies such as sub-pixel scanning, synthetic aperture, and reflective tomography enable long-range super-resolution imaging by breaking through the physical limitations of conventional LiDAR systems. This paper begins by summarizing and sorting the critical problems associated with LiDAR super-resolution technology. The key technological research progress is then reported, typical experimental systems and experimental results are analyzed, and the characteristics, advantages, and shortcomings of each system are described with respect to requirements of space exploration, remote sensing, and mapping missions. Finally, the application prospects and development trends are presented.
With the expansion of China’s space interests and the growth in the scale of on-orbit assets, high-precision detection of dark and weak targets in noncooperative space has become the core bottleneck in space security defense and debris removal. Traditional optical or radar detection technologies are limited by diffraction limit and signal-to-noise ratio constraints, and the detection and identification accuracy of “fast, far, small, and dark” targets is insufficient. Light Detection and Ranging (LiDAR), with its high precision and anti-jamming advantages, has gradually become the core technical means of accurately detecting space targets. Technologies such as sub-pixel scanning, synthetic aperture, and reflective tomography enable long-range super-resolution imaging by breaking through the physical limitations of conventional LiDAR systems. This paper begins by summarizing and sorting the critical problems associated with LiDAR super-resolution technology. The key technological research progress is then reported, typical experimental systems and experimental results are analyzed, and the characteristics, advantages, and shortcomings of each system are described with respect to requirements of space exploration, remote sensing, and mapping missions. Finally, the application prospects and development trends are presented.
2025, 14(3): 528-547.
As an important method of 3D (Three-Dimensional) data processing, point cloud fusion technology has shown great potential and promising applications in many fields. This paper systematically reviews the basic concepts, commonly used techniques, and applications of point cloud fusion and thoroughly analyzes the current status and future development trends of various fusion methods. Additionally, the paper explores the practical applications and challenges of point cloud fusion in fields such as autonomous driving, architecture, and robotics. Special attention is given to balancing algorithmic complexity with fusion accuracy, particularly in addressing issues like noise, data sparsity, and uneven point cloud density. This study serves as a strong reference for the future development of point cloud fusion technology by providing a comprehensive overview of the existing research progress and identifying possible research directions for further improving the accuracy, robustness, and efficiency of fusion algorithms.
As an important method of 3D (Three-Dimensional) data processing, point cloud fusion technology has shown great potential and promising applications in many fields. This paper systematically reviews the basic concepts, commonly used techniques, and applications of point cloud fusion and thoroughly analyzes the current status and future development trends of various fusion methods. Additionally, the paper explores the practical applications and challenges of point cloud fusion in fields such as autonomous driving, architecture, and robotics. Special attention is given to balancing algorithmic complexity with fusion accuracy, particularly in addressing issues like noise, data sparsity, and uneven point cloud density. This study serves as a strong reference for the future development of point cloud fusion technology by providing a comprehensive overview of the existing research progress and identifying possible research directions for further improving the accuracy, robustness, and efficiency of fusion algorithms.
2025, 14(3): 548-561.
Small-footprint full-waveform Light Detection And Ranging (LiDAR) exhibits significant application potential owing to its high penetration capability and ability to capture complete echo data. However, the efficient and accurate processing of massive echo signals remains a crucial challenge for practical use, particularly in advancing waveform decomposition technology. In small-footprint full-waveform LiDAR systems, most echoes are single-target, while only multi-target echoes require detailed decomposition. Current solutions often sacrifice precision by employing simplified rapid waveform decomposition algorithms or process all echoes indiscriminately, resulting in low efficiency and the inability to balance accuracy and speed effectively. This study proposes a spatiotemporal coupling model-driven lightweight algorithm for detecting multi-target echoes in small-footprint full-waveform LiDAR. For the first time, it achieves efficient and accurate detection of multi-target echoes from waveform data with unknown echo counts. The proposed method eliminates redundant computations caused by indiscriminate processing of single-target echoes, significantly reducing waveform decomposition iterations. The technical contributions include constructing a spatiotemporal coupling echo signal model that captures the spatiotemporal characteristics of echo transmission, implementing model-driven lightweight waveform parameter estimation through double Gaussian function superposition fitting, and introducing an adaptive correlation discrimination method based on a signal-to-noise ratio approach. By leveraging the consistency of system-emitted pulses, the proposed method enables lightweight yet accurate multi-target echo detection. Experimental results on terrestrial and airborne waveform datasets demonstrate that our algorithm achieves 98.4% detection accuracy with a 93.1% recall rate. When integrated with four waveform decomposition methods, it improves processing efficiency by 2–3 times. The efficiency gain becomes even more pronounced as the proportion of single-target echoes increases.
Small-footprint full-waveform Light Detection And Ranging (LiDAR) exhibits significant application potential owing to its high penetration capability and ability to capture complete echo data. However, the efficient and accurate processing of massive echo signals remains a crucial challenge for practical use, particularly in advancing waveform decomposition technology. In small-footprint full-waveform LiDAR systems, most echoes are single-target, while only multi-target echoes require detailed decomposition. Current solutions often sacrifice precision by employing simplified rapid waveform decomposition algorithms or process all echoes indiscriminately, resulting in low efficiency and the inability to balance accuracy and speed effectively. This study proposes a spatiotemporal coupling model-driven lightweight algorithm for detecting multi-target echoes in small-footprint full-waveform LiDAR. For the first time, it achieves efficient and accurate detection of multi-target echoes from waveform data with unknown echo counts. The proposed method eliminates redundant computations caused by indiscriminate processing of single-target echoes, significantly reducing waveform decomposition iterations. The technical contributions include constructing a spatiotemporal coupling echo signal model that captures the spatiotemporal characteristics of echo transmission, implementing model-driven lightweight waveform parameter estimation through double Gaussian function superposition fitting, and introducing an adaptive correlation discrimination method based on a signal-to-noise ratio approach. By leveraging the consistency of system-emitted pulses, the proposed method enables lightweight yet accurate multi-target echo detection. Experimental results on terrestrial and airborne waveform datasets demonstrate that our algorithm achieves 98.4% detection accuracy with a 93.1% recall rate. When integrated with four waveform decomposition methods, it improves processing efficiency by 2–3 times. The efficiency gain becomes even more pronounced as the proportion of single-target echoes increases.
2025, 14(3): 562-575.
The vertical characteristics of biological optical parameters in the upper ocean are essential for evaluating marine primary productivity and the carbon cycle. Although ocean lidar can effectively detect these parameters, the inversion results are usually highly biased due to the regional differences in the adaptability of empirical models. This study uses multiplatform LiDAR observations collected in a certain sea area of China (2023–2024), combined with a region-adaptive bio-optical model, to achieve high-precision profiling of bio-optical parameters in the region. The derived vertical profiles of chlorophyll-a concentration showed strong agreement with in-situ measurements, with a coefficient of determination (R2) of 0.84 and an average root mean square error of 0.14 μg·L–1. Further quantitative analysis using an error transfer model revealed that differences in band-specific optical sensitivity considerably affected error distribution. The effective detection depth in the blue band was 70 m, notably higher than the 58 m depth in the green band. In addition, at the subsurface chlorophyll maximum layer, the inversion bias in the blue band was 0.18 μg·L–1 lower than that in the green band, highlighting the intrinsic relationship between the optical characteristics of each wavelength and its associated bias. This result provides an effective method for improving the reliability of profile inversion of bio-optical parameters in complex waters and performing error analysis.
The vertical characteristics of biological optical parameters in the upper ocean are essential for evaluating marine primary productivity and the carbon cycle. Although ocean lidar can effectively detect these parameters, the inversion results are usually highly biased due to the regional differences in the adaptability of empirical models. This study uses multiplatform LiDAR observations collected in a certain sea area of China (2023–2024), combined with a region-adaptive bio-optical model, to achieve high-precision profiling of bio-optical parameters in the region. The derived vertical profiles of chlorophyll-a concentration showed strong agreement with in-situ measurements, with a coefficient of determination (R2) of 0.84 and an average root mean square error of 0.14 μg·L–1. Further quantitative analysis using an error transfer model revealed that differences in band-specific optical sensitivity considerably affected error distribution. The effective detection depth in the blue band was 70 m, notably higher than the 58 m depth in the green band. In addition, at the subsurface chlorophyll maximum layer, the inversion bias in the blue band was 0.18 μg·L–1 lower than that in the green band, highlighting the intrinsic relationship between the optical characteristics of each wavelength and its associated bias. This result provides an effective method for improving the reliability of profile inversion of bio-optical parameters in complex waters and performing error analysis.
2025, 14(3): 576-588.
Antarctic Digital Elevation Models (DEMs) provide critical topographic support for polar scientific expeditions and enable the estimation of melt pond volumes. However, conventional ground calibration methods face implementation challenges in extreme Antarctic environments. Spaceborne Light Detection And Ranging (LiDAR) effectively addresses this limitation by directly acquiring high-precision surface elevation data. ICESat-2, a next-generation laser altimetry satellite, features an exceptionally small laser footprint spacing of merely 0.7 m. The elevation data products of ICESat-2 over the Antarctic ice sheet achieve centimeter-level accuracy using the Reference Elevation Model of Antarctica (REMA) source data. This study first validated the elevation accuracy of the ICESat-2 ATL06 (Advanced Topographic Laser Altimeter System Land Ice Height) data products using the IDHDT4 (IceBridge HiCARS Depth Digitizer Time Series, Version 4) data from the 2015 Operation IceBridge campaign of NASA in the McMurdo Dry Valleys region and mitigated disturbances from cloud cover, snowfall, and other factors through a quality control algorithm. Building upon this validation, this study systematically assessed the elevation accuracy of the 32 m resolution REMA DEM across selected low-ablation regions of the Antarctic ice sheet, delineated according to Antarctic drainage basin boundaries, using the ATL06 data as a reference. Results showed that REMA DEM achieves submeter accuracy (comparable to laser altimetry precision) in flat terrains with slopes below 5°, with a Root-Mean-Square Error (RMSE) of 0.72 m and a Mean Absolute Error (MAE) of 0.31 m. For moderate slopes of 5°–10°, the RMSE and MAE increased to 1.91 and 1.06 m, respectively; meanwhile, slopes of 10°–15° yielded values of 2.30 m (RMSE) and 1.57 m (MAE). Even at steeper slopes of 30°, the elevation error remained controlled, with the RMSE not exceeding 3.5 m. This study further quantified the impact of ground track orientation relative to slope aspect and seasonal variations. Track-aspect angles perpendicular to slopes intensify errors (e.g., RMSE increases by 170% at a slope of 15°), whereas seasonal differences in elevation errors remain minimal (i.e., <5%). The validation framework demonstrates the robustness of REMA DEM across diverse Antarctic terrains, providing a theoretical foundation for different applications, such as lake ice surface bathymetry inversion
Antarctic Digital Elevation Models (DEMs) provide critical topographic support for polar scientific expeditions and enable the estimation of melt pond volumes. However, conventional ground calibration methods face implementation challenges in extreme Antarctic environments. Spaceborne Light Detection And Ranging (LiDAR) effectively addresses this limitation by directly acquiring high-precision surface elevation data. ICESat-2, a next-generation laser altimetry satellite, features an exceptionally small laser footprint spacing of merely 0.7 m. The elevation data products of ICESat-2 over the Antarctic ice sheet achieve centimeter-level accuracy using the Reference Elevation Model of Antarctica (REMA) source data. This study first validated the elevation accuracy of the ICESat-2 ATL06 (Advanced Topographic Laser Altimeter System Land Ice Height) data products using the IDHDT4 (IceBridge HiCARS Depth Digitizer Time Series, Version 4) data from the 2015 Operation IceBridge campaign of NASA in the McMurdo Dry Valleys region and mitigated disturbances from cloud cover, snowfall, and other factors through a quality control algorithm. Building upon this validation, this study systematically assessed the elevation accuracy of the 32 m resolution REMA DEM across selected low-ablation regions of the Antarctic ice sheet, delineated according to Antarctic drainage basin boundaries, using the ATL06 data as a reference. Results showed that REMA DEM achieves submeter accuracy (comparable to laser altimetry precision) in flat terrains with slopes below 5°, with a Root-Mean-Square Error (RMSE) of 0.72 m and a Mean Absolute Error (MAE) of 0.31 m. For moderate slopes of 5°–10°, the RMSE and MAE increased to 1.91 and 1.06 m, respectively; meanwhile, slopes of 10°–15° yielded values of 2.30 m (RMSE) and 1.57 m (MAE). Even at steeper slopes of 30°, the elevation error remained controlled, with the RMSE not exceeding 3.5 m. This study further quantified the impact of ground track orientation relative to slope aspect and seasonal variations. Track-aspect angles perpendicular to slopes intensify errors (e.g., RMSE increases by 170% at a slope of 15°), whereas seasonal differences in elevation errors remain minimal (i.e., <5%). The validation framework demonstrates the robustness of REMA DEM across diverse Antarctic terrains, providing a theoretical foundation for different applications, such as lake ice surface bathymetry inversion
2025, 14(3): 589-601.
In recent years, surface ship target tracking has been an important issue that needs to be solved in autonomous ship navigation. For three-dimensional environmental perception, LiDAR has the characteristics of high resolution and high precision, for three-dimensional environmental perception. By adding one-dimensional scanning, long-line array LiDAR has a larger field of view compared with single point and area array LiDAR, offering unique advantages in environmental perception. Owing to the inconsistency between the characteristics of surface ships and ground target, and the lack of relevant data sets, the current commonly used fitting methods cannot effectively perceive surface target characteristics. In this paper, an efficient target tracking method for ships is proposed based on the characteristics of single-photon point clouds and long-distance target detection. This method is based on the synchronous clustering and denoising of neighboring points; it uses the prior knowledge of the geometric features of ships to fit through the extraction of ship feature points and surfaces, further reducing the influence of noise. Combined with the extended Kalman filter and velocity estimation method, the real-time and stable trajectory tracking of a 600 m target is realized. The root mean square error of tracking is 0.5 m, with a single-frame processing time of 1.02 s, which meets real-time engineering requirements. The proposed method has also been tested in a complex environment and has a good tracking effect for large ships, which is better than the common fitting tracking method. This provides better information for the subsequent autonomous navigation of intelligent ships, and realizes better obstacle avoidance and path planning for ships.
In recent years, surface ship target tracking has been an important issue that needs to be solved in autonomous ship navigation. For three-dimensional environmental perception, LiDAR has the characteristics of high resolution and high precision, for three-dimensional environmental perception. By adding one-dimensional scanning, long-line array LiDAR has a larger field of view compared with single point and area array LiDAR, offering unique advantages in environmental perception. Owing to the inconsistency between the characteristics of surface ships and ground target, and the lack of relevant data sets, the current commonly used fitting methods cannot effectively perceive surface target characteristics. In this paper, an efficient target tracking method for ships is proposed based on the characteristics of single-photon point clouds and long-distance target detection. This method is based on the synchronous clustering and denoising of neighboring points; it uses the prior knowledge of the geometric features of ships to fit through the extraction of ship feature points and surfaces, further reducing the influence of noise. Combined with the extended Kalman filter and velocity estimation method, the real-time and stable trajectory tracking of a 600 m target is realized. The root mean square error of tracking is 0.5 m, with a single-frame processing time of 1.02 s, which meets real-time engineering requirements. The proposed method has also been tested in a complex environment and has a good tracking effect for large ships, which is better than the common fitting tracking method. This provides better information for the subsequent autonomous navigation of intelligent ships, and realizes better obstacle avoidance and path planning for ships.
2025, 14(3): 602-615.
Hyperspectral LiDAR (HSL) can obtain high precision and resolution spatial data along with the spectral information of the target, which can provide effective and multidimensional data for various research and application fields. However, differences in transmitting signal intensities of HSL at various wavelengths lead to variations in corresponding echo intensities, making it challenging to directly reconstruct accurate optical characteristics (reflectance spectral profile) of the target with echo intensities. To obtain the target reflectance spectral profile, a common solution is to correct the echo intensity (standard reference correction method) using standard diffuse reflectance whiteboards. However, in complex detection environments, whiteboards are susceptible to contamination, and the transmitting intensity of the laser may fluctuate due to changes in the environment and equipment conditions, which may potentially impact the calculation accuracy. The direct transmission of information from the full-waveform signals to the reconstruction of the reflectance spectral profiles is a more efficient approach. Therefore, we propose an echo intensity correction method based on HSL full-waveform data for the rapid generation of reflectance spectral profiles of targets. The initial step is to conduct a theoretical analysis that illustrates the similarity between the echo signals and the transmitting signals in terms of their waveforms. A skew-normal Gaussian function is then employed to fit the transmitting and echo signals of the HSL full waveform. Thereafter, the transmit-to-echo signal peak ratios (normalization factors) of the standard diffuse reflectance whiteboard at different wavelengths are calculated under ideal conditions. Finally, the reflectance spectral profile of the target is constructed by combining the normalization factor of the standard diffuse reflectance whiteboard with that of the target. To verify the effectiveness of the proposed method, we conducted experiments to compare the reflectance spectral profiles calculated using the standard reference correction method. Moreover, we performed wood-leaf separation and target classification experiments to assess its reliability and usability. The experimental results reveal the following: (1) The reconstructed reflectance spectral profiles of the target can be obtained by correcting the echo intensity with the transmitting signals, which is similar to that obtained by the standard reference correction method. Moreover, it demonstrates excellent stability under various temperatures and lighting conditions. Compared with the standard reference correction method, this approach effectively overcomes the influence of laser emission energy fluctuations, thereby considerably improving the measurement accuracy and consistency of reflectance spectral curves, especially under prolonged HSL operation conditions. (2) The wood-leaf separation and the multiple target classification can be conducted using the reconstructed target reflectance spectral profiles, with a classification accuracy of over 90%. Overall, the proposed method simplifies the correction of echo intensity for full-waveform HSL, which is suitable for the rapid reconstruction of target hyperspectral information during data acquisition.
Hyperspectral LiDAR (HSL) can obtain high precision and resolution spatial data along with the spectral information of the target, which can provide effective and multidimensional data for various research and application fields. However, differences in transmitting signal intensities of HSL at various wavelengths lead to variations in corresponding echo intensities, making it challenging to directly reconstruct accurate optical characteristics (reflectance spectral profile) of the target with echo intensities. To obtain the target reflectance spectral profile, a common solution is to correct the echo intensity (standard reference correction method) using standard diffuse reflectance whiteboards. However, in complex detection environments, whiteboards are susceptible to contamination, and the transmitting intensity of the laser may fluctuate due to changes in the environment and equipment conditions, which may potentially impact the calculation accuracy. The direct transmission of information from the full-waveform signals to the reconstruction of the reflectance spectral profiles is a more efficient approach. Therefore, we propose an echo intensity correction method based on HSL full-waveform data for the rapid generation of reflectance spectral profiles of targets. The initial step is to conduct a theoretical analysis that illustrates the similarity between the echo signals and the transmitting signals in terms of their waveforms. A skew-normal Gaussian function is then employed to fit the transmitting and echo signals of the HSL full waveform. Thereafter, the transmit-to-echo signal peak ratios (normalization factors) of the standard diffuse reflectance whiteboard at different wavelengths are calculated under ideal conditions. Finally, the reflectance spectral profile of the target is constructed by combining the normalization factor of the standard diffuse reflectance whiteboard with that of the target. To verify the effectiveness of the proposed method, we conducted experiments to compare the reflectance spectral profiles calculated using the standard reference correction method. Moreover, we performed wood-leaf separation and target classification experiments to assess its reliability and usability. The experimental results reveal the following: (1) The reconstructed reflectance spectral profiles of the target can be obtained by correcting the echo intensity with the transmitting signals, which is similar to that obtained by the standard reference correction method. Moreover, it demonstrates excellent stability under various temperatures and lighting conditions. Compared with the standard reference correction method, this approach effectively overcomes the influence of laser emission energy fluctuations, thereby considerably improving the measurement accuracy and consistency of reflectance spectral curves, especially under prolonged HSL operation conditions. (2) The wood-leaf separation and the multiple target classification can be conducted using the reconstructed target reflectance spectral profiles, with a classification accuracy of over 90%. Overall, the proposed method simplifies the correction of echo intensity for full-waveform HSL, which is suitable for the rapid reconstruction of target hyperspectral information during data acquisition.
2025, 14(3): 616-628.
Light Detection And Ranging (LiDAR) systems lack texture and color information, while cameras lack depth information. Thus, the information obtained from LiDAR and cameras is highly complementary. Therefore, combining these two types of sensors can obtain rich observation data and improve the accuracy and stability of environmental perception. The accurate joint calibration of the external parameters of these two types of sensors is the premise of data fusion. At present, most joint calibration methods need to be processed through target calibration and manual point selection. This makes it impossible to use them in dynamic application scenarios. This paper presents a ResCalib deep neural network model, which can be used to solve the problem of the online joint calibration of LiDAR and a camera. The method uses LiDAR point clouds, monocular images, and in-camera parameter matrices as the input to achieve the external parameters solving of LiDAR and cameras; however, the method has low dependence on external features or targets. ResCalib is a geometrically supervised deep neural network that automatically estimates the six-degree-of-freedom external parameter relationship between LiDAR and cameras by implementing supervised learning to maximize the geometric and photometric consistencies of input images and point clouds. Experiments show that the proposed method can correct errors in calibrating rotation by ±10° and translation by ±0.2 m. The average absolute errors of the rotation and translation components of the calibration solution are 0.35° and 0.032 m, respectively, and the time required for single-group calibration is 0.018 s, which provides technical support for realizing automatic joint calibration in a dynamic environment.
Light Detection And Ranging (LiDAR) systems lack texture and color information, while cameras lack depth information. Thus, the information obtained from LiDAR and cameras is highly complementary. Therefore, combining these two types of sensors can obtain rich observation data and improve the accuracy and stability of environmental perception. The accurate joint calibration of the external parameters of these two types of sensors is the premise of data fusion. At present, most joint calibration methods need to be processed through target calibration and manual point selection. This makes it impossible to use them in dynamic application scenarios. This paper presents a ResCalib deep neural network model, which can be used to solve the problem of the online joint calibration of LiDAR and a camera. The method uses LiDAR point clouds, monocular images, and in-camera parameter matrices as the input to achieve the external parameters solving of LiDAR and cameras; however, the method has low dependence on external features or targets. ResCalib is a geometrically supervised deep neural network that automatically estimates the six-degree-of-freedom external parameter relationship between LiDAR and cameras by implementing supervised learning to maximize the geometric and photometric consistencies of input images and point clouds. Experiments show that the proposed method can correct errors in calibrating rotation by ±10° and translation by ±0.2 m. The average absolute errors of the rotation and translation components of the calibration solution are 0.35° and 0.032 m, respectively, and the time required for single-group calibration is 0.018 s, which provides technical support for realizing automatic joint calibration in a dynamic environment.
2025, 14(3): 629-640.
To address the issue of LiDAR’s low turbulence recognition rate at airports in low-altitude areas, a clear air turbulence recognition method based on an improved Squeeze-and-Excitation Residual Network with 50 layers (SE-ResNet50) is proposed. By introducing the squeeze-and-excitation module and improving the network structure, the model’s excessive sensitivity to feature location is reduced, thereby enabling the network to selectively highlight useful information features during the learning process. A sample dataset was established using measured data from Lanzhou Zhongchuan International Airport; for model training, a balanced dataset was created by extracting an equal amount of weak, moderate, and strong turbulence data based on the turbulence classification level. Under the same experimental conditions, the recognition accuracy of the improved SE-ResNet50 was increased by 7.44%, 6.52%, and 4.11% compared with the convolutional neural network, MobileNetV2, and ShuffleNetV1 networks, respectively. A comparison of the confusion matrices generated by each model showed that the accuracy of the proposed method reached 95%, verifying the feasibility of the proposed method.
To address the issue of LiDAR’s low turbulence recognition rate at airports in low-altitude areas, a clear air turbulence recognition method based on an improved Squeeze-and-Excitation Residual Network with 50 layers (SE-ResNet50) is proposed. By introducing the squeeze-and-excitation module and improving the network structure, the model’s excessive sensitivity to feature location is reduced, thereby enabling the network to selectively highlight useful information features during the learning process. A sample dataset was established using measured data from Lanzhou Zhongchuan International Airport; for model training, a balanced dataset was created by extracting an equal amount of weak, moderate, and strong turbulence data based on the turbulence classification level. Under the same experimental conditions, the recognition accuracy of the improved SE-ResNet50 was increased by 7.44%, 6.52%, and 4.11% compared with the convolutional neural network, MobileNetV2, and ShuffleNetV1 networks, respectively. A comparison of the confusion matrices generated by each model showed that the accuracy of the proposed method reached 95%, verifying the feasibility of the proposed method.
2025, 14(3): 641-650.
The airport docking guidance system is essential for enhancing airport safety and operational efficiency. This study introduces a deep learning-based point cloud completion network designed for accurate aircraft localization using LiDAR technology. Initially, the aircraft parking process is simulated in a realistic virtual environment to generate complete point cloud data. Subsequently, partial point clouds caused by occlusions or sensor limitations are processed through the proposed network to reconstruct their complete geometric structures. Then the restored point cloud is aligned with a predefined aircraft model, enabling precise calculation of the aircraft’s center coordinates in the simulated coordinate system through spatial transformation. Experimental results demonstrate that the network effectively recovers structural details from incomplete point clouds, enabling accurate computation of aircraft centroid coordinates. This approach achieves high-precision position detection for aircraft during docking, showing significant potential for practical airport applications. The codes are available at: https://www.scidb.cn/anonymous/UXZFZkFm.
The airport docking guidance system is essential for enhancing airport safety and operational efficiency. This study introduces a deep learning-based point cloud completion network designed for accurate aircraft localization using LiDAR technology. Initially, the aircraft parking process is simulated in a realistic virtual environment to generate complete point cloud data. Subsequently, partial point clouds caused by occlusions or sensor limitations are processed through the proposed network to reconstruct their complete geometric structures. Then the restored point cloud is aligned with a predefined aircraft model, enabling precise calculation of the aircraft’s center coordinates in the simulated coordinate system through spatial transformation. Experimental results demonstrate that the network effectively recovers structural details from incomplete point clouds, enabling accurate computation of aircraft centroid coordinates. This approach achieves high-precision position detection for aircraft during docking, showing significant potential for practical airport applications. The codes are available at: https://www.scidb.cn/anonymous/UXZFZkFm.
2025, 14(3): 651-663.
This paper addresses the challenges of adapting complex perception systems to perform core tasks such as detection, tracking, and countermeasures in highly dynamic and adversarial battlefield environments. We propose an information-driven theoretical model and a systematic methodology for system construction. This study introduces an information-driven theoretical model and construction methodology for such systems. Specifically, a multi-layered information description framework is introduced to overcome the application barriers of traditional static modeling and fixed-pattern design. This framework is based on syntax, semantics, and pragmatics and is designed to overcome the limitations of single syntactic structures and surface-level semantics. A dynamic evolution architecture is incorporated into the system. The theoretical achievements are also applied to the practice of distributed radar detection systems. Moreover, a structured hierarchical optimization algorithm with a finite-scenario interactive learning mechanism is designed to achieve ordered system organization and capability emergence, thereby addressing the complexity of system optimization. This study provides a theoretical framework and technical approach for the design of intelligent perception systems in complex battlefield environments.
This paper addresses the challenges of adapting complex perception systems to perform core tasks such as detection, tracking, and countermeasures in highly dynamic and adversarial battlefield environments. We propose an information-driven theoretical model and a systematic methodology for system construction. This study introduces an information-driven theoretical model and construction methodology for such systems. Specifically, a multi-layered information description framework is introduced to overcome the application barriers of traditional static modeling and fixed-pattern design. This framework is based on syntax, semantics, and pragmatics and is designed to overcome the limitations of single syntactic structures and surface-level semantics. A dynamic evolution architecture is incorporated into the system. The theoretical achievements are also applied to the practice of distributed radar detection systems. Moreover, a structured hierarchical optimization algorithm with a finite-scenario interactive learning mechanism is designed to achieve ordered system organization and capability emergence, thereby addressing the complexity of system optimization. This study provides a theoretical framework and technical approach for the design of intelligent perception systems in complex battlefield environments.
2025, 14(3): 664-696.
Electromagnetic (EM) metasurfaces are a novel type of artificial EM material exhibiting great advantages for wireless communication and signal processing. By introducing external excitation (mechanical, thermal, electrical, optical, and magnetic excitations), the EM metasurface realizes a more flexible dynamic control of the EM response. On the basis of the dynamic control method, the EM metasurface can accurately control the phase, amplitude, polarization mode, propagation mode, and other characteristics of EM waves to realize wavefront control in different application scenarios. In this paper, we first summarize the research progress of dynamic control technology for EM metasurfaces. Then, the research status of EM metasurfaces in the fields of holographic imaging, polarization conversion, metalensing, beam steering, and intelligent systems based on the application scenarios is discussed. Finally, the development modes of EM metasurfaces and the development trends of intelligent control in the future are summarized and explored.
Electromagnetic (EM) metasurfaces are a novel type of artificial EM material exhibiting great advantages for wireless communication and signal processing. By introducing external excitation (mechanical, thermal, electrical, optical, and magnetic excitations), the EM metasurface realizes a more flexible dynamic control of the EM response. On the basis of the dynamic control method, the EM metasurface can accurately control the phase, amplitude, polarization mode, propagation mode, and other characteristics of EM waves to realize wavefront control in different application scenarios. In this paper, we first summarize the research progress of dynamic control technology for EM metasurfaces. Then, the research status of EM metasurfaces in the fields of holographic imaging, polarization conversion, metalensing, beam steering, and intelligent systems based on the application scenarios is discussed. Finally, the development modes of EM metasurfaces and the development trends of intelligent control in the future are summarized and explored.
2025, 14(3): 697-711.
In this paper, a Frequency Diverse Array (FDA)-based Synthetic-Aperture Radar (FDA-SAR) imaging method based on sub-band shifting and splicing is proposed to address the conflicting issues associated with multi-mode SAR imaging in parameter design, such as those related to resolution and imaging swath. Using the multiple-sub-band concurrent mode of a FDA radar, a radar waveform with an adjustable bandwidth is designed. The time-frequency-domain expressions of the synthesized signals with arbitrary bandwidths are derived in detail, enabling compensation for the involved azimuth-time-delay differences and inconsistent frequency bands. The effect of the spectrum distribution of the synthesized signals on the imaging performance is analyzed, and spectrum synthesis based on non-uniform sub-band shifting is adopted, which reduces the peak sidelobe level and improves the imaging performance. Finally, simulations verify the effectiveness of the proposed method to simultaneously achieve signal-level fusion processing for coarse-resolution imaging of large observation scenes and fine-resolution imaging of key areas.
In this paper, a Frequency Diverse Array (FDA)-based Synthetic-Aperture Radar (FDA-SAR) imaging method based on sub-band shifting and splicing is proposed to address the conflicting issues associated with multi-mode SAR imaging in parameter design, such as those related to resolution and imaging swath. Using the multiple-sub-band concurrent mode of a FDA radar, a radar waveform with an adjustable bandwidth is designed. The time-frequency-domain expressions of the synthesized signals with arbitrary bandwidths are derived in detail, enabling compensation for the involved azimuth-time-delay differences and inconsistent frequency bands. The effect of the spectrum distribution of the synthesized signals on the imaging performance is analyzed, and spectrum synthesis based on non-uniform sub-band shifting is adopted, which reduces the peak sidelobe level and improves the imaging performance. Finally, simulations verify the effectiveness of the proposed method to simultaneously achieve signal-level fusion processing for coarse-resolution imaging of large observation scenes and fine-resolution imaging of key areas.
2025, 14(3): 712-734.
Synthetic Aperture Radar (SAR) ocean remote sensing simulation is an important analytical tool for designing SAR systems for ocean applications. It can also provide training samples for detecting and recognizing SAR images of complex ocean phenomena. Therefore, it plays an important role in the design and application of SAR ocean remote sensing systems. The motion, time-varying, and decoherence characteristics of the sea surface caused the simulation difficulty and calculation amount of SAR ocean remote sensing to be much larger than those of fixed land targets. Therefore, improving the simulation efficiency while ensuring the simulation accuracy is key to achieving high-precision and high-efficiency simulation of SAR ocean imaging. This study introduces the main methods, development status, and main problems of dynamic ocean SAR imaging simulation and provides methods for realizing key problems in high-precision simulation of dynamic ocean SAR imaging. The method can complete the simulation of a 4 m resolution at a 400 km2 scene within 10 min while ensuring high fidelity. Under typical working conditions, the spectral peak error of a simulated SAR image is 3%, and the spectral width error is 4%. The typical applications of dynamic ocean surface SAR imaging simulation in wave spectrum inversion, wave texture suppression based on depth cancellation networks, and ship wake detection based on the Wake2Wake network are introduced. On the one hand, these applications verify that the fidelity of the high-precision simulation of dynamic sea SAR imaging presented in this study can satisfy the requirements of intelligent simulation training. On the other hand, the high-precision simulation offers a good prospect for intelligent application of SAR ocean images and can be an important method for providing samples for intelligent application of SAR ocean remote sensing.
Synthetic Aperture Radar (SAR) ocean remote sensing simulation is an important analytical tool for designing SAR systems for ocean applications. It can also provide training samples for detecting and recognizing SAR images of complex ocean phenomena. Therefore, it plays an important role in the design and application of SAR ocean remote sensing systems. The motion, time-varying, and decoherence characteristics of the sea surface caused the simulation difficulty and calculation amount of SAR ocean remote sensing to be much larger than those of fixed land targets. Therefore, improving the simulation efficiency while ensuring the simulation accuracy is key to achieving high-precision and high-efficiency simulation of SAR ocean imaging. This study introduces the main methods, development status, and main problems of dynamic ocean SAR imaging simulation and provides methods for realizing key problems in high-precision simulation of dynamic ocean SAR imaging. The method can complete the simulation of a 4 m resolution at a 400 km2 scene within 10 min while ensuring high fidelity. Under typical working conditions, the spectral peak error of a simulated SAR image is 3%, and the spectral width error is 4%. The typical applications of dynamic ocean surface SAR imaging simulation in wave spectrum inversion, wave texture suppression based on depth cancellation networks, and ship wake detection based on the Wake2Wake network are introduced. On the one hand, these applications verify that the fidelity of the high-precision simulation of dynamic sea SAR imaging presented in this study can satisfy the requirements of intelligent simulation training. On the other hand, the high-precision simulation offers a good prospect for intelligent application of SAR ocean images and can be an important method for providing samples for intelligent application of SAR ocean remote sensing.
2025, 14(3): 735-753.
Crop and soil parameters serve as fundamental indicators for characterizing crop growth status and monitoring vegetation dynamics. Radar remote sensing presents unique advantages, such as all-weather and day-and-night observation capabilities, as well as insensitivity to meteorological conditions. Furthermore, the penetration ability of microwaves enhances the sensitivity to soil parameter variations beneath crop canopies, demonstrating significant potential for retrieving crop and soil parameters. This article presents a comprehensive review and analysis of inversion models used for crop and soil parameters based on the microwave scattering theory. First, it discusses the evolution of microwave scattering models from theoretical frameworks to semiempirical approaches, demonstrating key trends in theoretical advancements and methodological refinements. Subsequently, it systematically examines inversion methods for crop parameters, soil parameters, and crop-soil interactions, revealing their underlying microwave scattering mechanisms. Finally, the article discusses current model limitations and proposes future research directions aligned with emerging technological developments to provide novel insights for subsequent investigations.
Crop and soil parameters serve as fundamental indicators for characterizing crop growth status and monitoring vegetation dynamics. Radar remote sensing presents unique advantages, such as all-weather and day-and-night observation capabilities, as well as insensitivity to meteorological conditions. Furthermore, the penetration ability of microwaves enhances the sensitivity to soil parameter variations beneath crop canopies, demonstrating significant potential for retrieving crop and soil parameters. This article presents a comprehensive review and analysis of inversion models used for crop and soil parameters based on the microwave scattering theory. First, it discusses the evolution of microwave scattering models from theoretical frameworks to semiempirical approaches, demonstrating key trends in theoretical advancements and methodological refinements. Subsequently, it systematically examines inversion methods for crop parameters, soil parameters, and crop-soil interactions, revealing their underlying microwave scattering mechanisms. Finally, the article discusses current model limitations and proposes future research directions aligned with emerging technological developments to provide novel insights for subsequent investigations.
2025, 14(3): 754-780.
Maritime target detection and identification technology are developed using large-scale, high-quality multi-sensor measurement data. Therefore, the Sea Detection Radar Data Sharing Program (SDRDSP) was upgraded to the Maritime Target Data Sharing Program (MTDSP), integrating multiple observation modalities, such as HH-polarized radar, VV-polarized radar, electro-optical devices, and Automatic Identification System (AIS) equipment to conduct multisource observation experiments on maritime vessel targets. The program collects various data types, including radar intermediate frequency/video echo slice data, visible and infrared imagery, AIS static and dynamic messages, and meteorological and hydrological data, covering representative sea conditions and multiple vessel types. A comprehensive multisource observation dataset was constructed, enabling the matching and annotation of multimodal data for the same target. Moreover, an automated data management system was implemented to support data storage, conditional retrieval, and batch export, providing a solid foundation for the automated acquisition, long-term accumulation, and efficient use of maritime target characteristic data. Based on this system and measured data, the time/frequency domain features of the same and different vessel targets under different sea states, attitudes, polarization conditions are compared and analyzed, and the statistical conclusion of the change in target features is obtained.
Maritime target detection and identification technology are developed using large-scale, high-quality multi-sensor measurement data. Therefore, the Sea Detection Radar Data Sharing Program (SDRDSP) was upgraded to the Maritime Target Data Sharing Program (MTDSP), integrating multiple observation modalities, such as HH-polarized radar, VV-polarized radar, electro-optical devices, and Automatic Identification System (AIS) equipment to conduct multisource observation experiments on maritime vessel targets. The program collects various data types, including radar intermediate frequency/video echo slice data, visible and infrared imagery, AIS static and dynamic messages, and meteorological and hydrological data, covering representative sea conditions and multiple vessel types. A comprehensive multisource observation dataset was constructed, enabling the matching and annotation of multimodal data for the same target. Moreover, an automated data management system was implemented to support data storage, conditional retrieval, and batch export, providing a solid foundation for the automated acquisition, long-term accumulation, and efficient use of maritime target characteristic data. Based on this system and measured data, the time/frequency domain features of the same and different vessel targets under different sea states, attitudes, polarization conditions are compared and analyzed, and the statistical conclusion of the change in target features is obtained.
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