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The ocean surface is a complicated dynamic system with considerable irregularity and nonrepetition in space and time. Sea clutter is the superposition of a large number of scatterer echoes generated by the radar electromagnetic signal irradiated to the sea surface, which is affected by wind, currents, waves, etc. and shows nonuniformity and nonsmoothness. The sea clutter signal has a certain interference effect on the detection of sea targets, especially under high sea conditions when the waves are furious, and the target signal is readily drowned out by the strong sea clutter signal, severely limiting the radar’s detection capability on sea targets. The investigation of sea clutter and target electromagnetic scattering properties serves as the foundation for improving the target detection capability in difficult marine environments. The formation of target echo data in the actual marine environment is of great significance for the analysis of sea clutter and target radar echo characteristics, as well as the supplementation of the actual measurement data set based on electromagnetic waves and the actual complex dynamic sea surface and target electromagnetic scattering mechanism. This study summarizes three key categories of echo simulation methods, analyzes the benefits, disadvantages, and adaptability of several categories of methods for the characteristics of the sea surface and target simulation scenarios, and provides some simulation results in order to make recent advancements and future trends of physics-based complex sea environment and target echo simulation methods more accessible to relevant researchers. It also introduces some echo datasets based on real measurements, which can facilitate scholars’ analysis of echo characteristics. Lastly, the trend toward developing complex sea surface and target echo simulation methods and characteristics for research is presented. The ocean surface is a complicated dynamic system with considerable irregularity and nonrepetition in space and time. Sea clutter is the superposition of a large number of scatterer echoes generated by the radar electromagnetic signal irradiated to the sea surface, which is affected by wind, currents, waves, etc. and shows nonuniformity and nonsmoothness. The sea clutter signal has a certain interference effect on the detection of sea targets, especially under high sea conditions when the waves are furious, and the target signal is readily drowned out by the strong sea clutter signal, severely limiting the radar’s detection capability on sea targets. The investigation of sea clutter and target electromagnetic scattering properties serves as the foundation for improving the target detection capability in difficult marine environments. The formation of target echo data in the actual marine environment is of great significance for the analysis of sea clutter and target radar echo characteristics, as well as the supplementation of the actual measurement data set based on electromagnetic waves and the actual complex dynamic sea surface and target electromagnetic scattering mechanism. This study summarizes three key categories of echo simulation methods, analyzes the benefits, disadvantages, and adaptability of several categories of methods for the characteristics of the sea surface and target simulation scenarios, and provides some simulation results in order to make recent advancements and future trends of physics-based complex sea environment and target echo simulation methods more accessible to relevant researchers. It also introduces some echo datasets based on real measurements, which can facilitate scholars’ analysis of echo characteristics. Lastly, the trend toward developing complex sea surface and target echo simulation methods and characteristics for research is presented.
Radar forward-looking imaging is important in many fields, such as precision guidance, autonomous landing, and terrain mapping. Due to the constraints of actual radar aperture, obtaining high-resolution images using the traditional forward-looking imaging method based on real beam scanning is challenging. Compared with the entire imaging scene, the objects of interest usually occupy only a small part of the area. This sparsity enables the use of compressed sensing to reconstruct high-resolution forward-looking images. However, the high noise in the radar echo affects the quality of the image generated by the compressed sensing method. Inspired by the low-rank property of the final image, this paper proposes a forward-looking super-resolution imaging model that combines sparse and low-rank properties. To effectively solve the dual constraint optimization problem in the proposed model, a forward-looking image reconstruction method based on an augmented Lagrange multiplier within the framework of the alternating direction multiplier method was proposed. Finally, the experimental results from simulation and real data show that the proposed method can effectively improve the azimuth resolution of radar forward-looking imaging while also being noise-robust. Radar forward-looking imaging is important in many fields, such as precision guidance, autonomous landing, and terrain mapping. Due to the constraints of actual radar aperture, obtaining high-resolution images using the traditional forward-looking imaging method based on real beam scanning is challenging. Compared with the entire imaging scene, the objects of interest usually occupy only a small part of the area. This sparsity enables the use of compressed sensing to reconstruct high-resolution forward-looking images. However, the high noise in the radar echo affects the quality of the image generated by the compressed sensing method. Inspired by the low-rank property of the final image, this paper proposes a forward-looking super-resolution imaging model that combines sparse and low-rank properties. To effectively solve the dual constraint optimization problem in the proposed model, a forward-looking image reconstruction method based on an augmented Lagrange multiplier within the framework of the alternating direction multiplier method was proposed. Finally, the experimental results from simulation and real data show that the proposed method can effectively improve the azimuth resolution of radar forward-looking imaging while also being noise-robust.
As compared with sparse scalar arrays and uniform diversely polarized arrays, sparse diversely polarized arrays show respectively advantages of possessing the ability of sensing the polarization state of source signals, avoiding polarization mismatch and increasing the degrees of freedom, reducing mutual coupling, decreasing hardware cost, etc. Therefore, the comprehensive research on sparse diversely polarized array is of great importance in both theory and realistic applications. The design architecture of sparse diversely polarized arrays, which are related not only to the location of the array elements, but also to the dipole/loop type, orientation, and polarization states of the antenna elements, are more diverse than those of sparse scalar arrays. This paper summarizes the relevant researches in this field in recent years, introduces and explores the mainstream sparse diversely polarized array structure optimization approaches from three aspects: non-uniformly sparse, uniformly sparse and mixed uniformly and non-uniformly sparse. Then the future development of sparse diversely polarized arrays is discussed in terms of deep learning-based optimization approach, sparse Multiple-Input Multiple-Output (MIMO) diversely polarized array, sparse Polarimetric Frequency Diverse Array (PFDA) radar and sparse PFDA-MIMO radar, sparse polarimetric reconfigurable intelligent surface, and the application of sparse diversely polarized array in complex indoor scenes, such as smart communications in house and Industrial Internet of Things. As compared with sparse scalar arrays and uniform diversely polarized arrays, sparse diversely polarized arrays show respectively advantages of possessing the ability of sensing the polarization state of source signals, avoiding polarization mismatch and increasing the degrees of freedom, reducing mutual coupling, decreasing hardware cost, etc. Therefore, the comprehensive research on sparse diversely polarized array is of great importance in both theory and realistic applications. The design architecture of sparse diversely polarized arrays, which are related not only to the location of the array elements, but also to the dipole/loop type, orientation, and polarization states of the antenna elements, are more diverse than those of sparse scalar arrays. This paper summarizes the relevant researches in this field in recent years, introduces and explores the mainstream sparse diversely polarized array structure optimization approaches from three aspects: non-uniformly sparse, uniformly sparse and mixed uniformly and non-uniformly sparse. Then the future development of sparse diversely polarized arrays is discussed in terms of deep learning-based optimization approach, sparse Multiple-Input Multiple-Output (MIMO) diversely polarized array, sparse Polarimetric Frequency Diverse Array (PFDA) radar and sparse PFDA-MIMO radar, sparse polarimetric reconfigurable intelligent surface, and the application of sparse diversely polarized array in complex indoor scenes, such as smart communications in house and Industrial Internet of Things.
In this study, a real-time dwell scheduling algorithm based on pulse interleaving is proposed for a distributed radar network system. A time pointer vector is introduced to indicate the moment when the dwell task with the highest synthetic priority should be chosen. This task is further allocated to the radar node with the lowest interleaving time utilization ratio, effectively reducing the time gaps during scheduling. Meanwhile, the pulse interleaving analysis determines whether the assigned dwell task can be scheduled successfully on the corresponding radar node. The time slot occupation matrix and energy assumption matrix are introduced to indicate the time and energy resource consumption of radar nodes, which not only simplifies the pulse interleaving analysis process but also enables pulse interleaving among the tasks with different pulse repetition intervals and numbers. Furthermore, to improve the efficiency of dwell scheduling, a threshold of interleaving time utilization ratio is set to adaptively choose the sliding step of the time pointer. The simulation results reveal that the proposed algorithm can execute real-time dwell scheduling for a distributed radar network system and achieve better scheduling performance than the existing dwell scheduling algorithm. In this study, a real-time dwell scheduling algorithm based on pulse interleaving is proposed for a distributed radar network system. A time pointer vector is introduced to indicate the moment when the dwell task with the highest synthetic priority should be chosen. This task is further allocated to the radar node with the lowest interleaving time utilization ratio, effectively reducing the time gaps during scheduling. Meanwhile, the pulse interleaving analysis determines whether the assigned dwell task can be scheduled successfully on the corresponding radar node. The time slot occupation matrix and energy assumption matrix are introduced to indicate the time and energy resource consumption of radar nodes, which not only simplifies the pulse interleaving analysis process but also enables pulse interleaving among the tasks with different pulse repetition intervals and numbers. Furthermore, to improve the efficiency of dwell scheduling, a threshold of interleaving time utilization ratio is set to adaptively choose the sliding step of the time pointer. The simulation results reveal that the proposed algorithm can execute real-time dwell scheduling for a distributed radar network system and achieve better scheduling performance than the existing dwell scheduling algorithm.
With the development in information technology and the change of air combat mode, Radar Warning Receiver (RWR) have become indispensable electronic warfare equipment for modern fighters. To better understand the airborne RWR system, this study divides the airborne RWR architecture into two stages from the perspective of receiver system. The characteristics and components of the architecture are analyzed. Then, this study elaborates on the signal processing flow of airborne RWR, and classifies the technologies and algorithms related to signal sorting, signal identification and threat assessment. Finally, this study systematically summarizes the challenges and future demand analysis of airborne RWR in complex battlefield environments and in dealing with new radar systems. With the development in information technology and the change of air combat mode, Radar Warning Receiver (RWR) have become indispensable electronic warfare equipment for modern fighters. To better understand the airborne RWR system, this study divides the airborne RWR architecture into two stages from the perspective of receiver system. The characteristics and components of the architecture are analyzed. Then, this study elaborates on the signal processing flow of airborne RWR, and classifies the technologies and algorithms related to signal sorting, signal identification and threat assessment. Finally, this study systematically summarizes the challenges and future demand analysis of airborne RWR in complex battlefield environments and in dealing with new radar systems.
Cognitive radar waveform design often relies on accurate clutter prior information. When prior information data is missing, the constructed clutter model will be severely mismatched, affecting the radar’s ability to suppress clutter. Aiming at the radar waveform optimization problem under missing clutter prior data, this paper establishes point and block-like missing scenarios under the completely random missing mechanism, designs a waveform optimization model with constant modulus and similarity constraints, and proposes a radar waveform training algorithm based on priority filling−reinforcement learning cascade optimization: that is, a cascade method in which the reinforcement learning agent interacts with the clutter environment repaired by a filling algorithm, with the optimization goal of maximizing the signal-to-noise ratio, and the optimal configuration strategy with waveform parameters is obtained through iterative training. Finally, simulations verify the superiority of the proposed algorithm under different missing probability conditions. The results show that the proposed algorithm outperforms the traditional non-cascading optimization algorithm, regarding clutter suppression and effectively improves the detection ability of radar. Cognitive radar waveform design often relies on accurate clutter prior information. When prior information data is missing, the constructed clutter model will be severely mismatched, affecting the radar’s ability to suppress clutter. Aiming at the radar waveform optimization problem under missing clutter prior data, this paper establishes point and block-like missing scenarios under the completely random missing mechanism, designs a waveform optimization model with constant modulus and similarity constraints, and proposes a radar waveform training algorithm based on priority filling−reinforcement learning cascade optimization: that is, a cascade method in which the reinforcement learning agent interacts with the clutter environment repaired by a filling algorithm, with the optimization goal of maximizing the signal-to-noise ratio, and the optimal configuration strategy with waveform parameters is obtained through iterative training. Finally, simulations verify the superiority of the proposed algorithm under different missing probability conditions. The results show that the proposed algorithm outperforms the traditional non-cascading optimization algorithm, regarding clutter suppression and effectively improves the detection ability of radar.
To refine the geometric parameters of satellite-based SAR images and improve the stereo positioning accuracy, a method of SAR elevation control point extraction combining multistrategy ATLAS data preference and image matching has been developed. This method is based on the concept of optical remote sensing image elevation control point extraction from satellite-based laser altimetry data. The method employs various strategies, such as non-night observation photon filtering, high confidence photon selection, SRTM DEM-assisted coarse difference rejection, and large eccentricity elliptical filtering kernel flat area photon screening. To extract laser elevation points with high quality and flat area from ATLAS data for ATL03 level products. Then the geocoding of the slant range SAR images is performed using the SRTM DEM. The local Google Earth images are selected as the footprint images according to the plane coordinates of the laser elevation points. The rank self-similarity descriptor is used to match the footprint images with the SAR geocoded images. The coordinates of the SAR images corresponding to the laser elevation points are obtained. Thus, SAR elevation control points are extracted. The extraction of GF-3 SAR elevation control points was performed using ATLAS data from two regions: Dengfeng, China, and Yokosuka, Japan. The geometric parameter refinement of SAR images using extracted elevation control points significantly improved the accuracy of stereo positioning and verified that the method for extracting elevation control points described in this paper is feasible and effective. To refine the geometric parameters of satellite-based SAR images and improve the stereo positioning accuracy, a method of SAR elevation control point extraction combining multistrategy ATLAS data preference and image matching has been developed. This method is based on the concept of optical remote sensing image elevation control point extraction from satellite-based laser altimetry data. The method employs various strategies, such as non-night observation photon filtering, high confidence photon selection, SRTM DEM-assisted coarse difference rejection, and large eccentricity elliptical filtering kernel flat area photon screening. To extract laser elevation points with high quality and flat area from ATLAS data for ATL03 level products. Then the geocoding of the slant range SAR images is performed using the SRTM DEM. The local Google Earth images are selected as the footprint images according to the plane coordinates of the laser elevation points. The rank self-similarity descriptor is used to match the footprint images with the SAR geocoded images. The coordinates of the SAR images corresponding to the laser elevation points are obtained. Thus, SAR elevation control points are extracted. The extraction of GF-3 SAR elevation control points was performed using ATLAS data from two regions: Dengfeng, China, and Yokosuka, Japan. The geometric parameter refinement of SAR images using extracted elevation control points significantly improved the accuracy of stereo positioning and verified that the method for extracting elevation control points described in this paper is feasible and effective.
Migratory pests are sudden outbreaks and widespread, putting national food security at risk. Entomological radar is most effective segment in monitoring insect migration, providing critical information for early warning and pest control. Traditional entomological radar can measure biological parameters such as mass and orientation using a low-resolution waveform and rotating linear polarization antenna. The new entomological radar uses a stepped chirp high-resolution waveform and instantaneous fully polarimetric system, which can considerably improve the accuracy of measuring insect biological data. However, in addition to the traditional polarization measurement errors, the stepped chirp waveform introduces new multiplicative error components to different polarization channels, resulting in a more complex imbalance between polarization channels, which requires high-precision polarization calibration. In response to the above issues, the fully polarimetric measurement model is optimized according to the characteristics of the high-resolution system, and a high-resolution system polarization error estimation method based on a sphere and a wire is proposed in this study, which can complete polarization calibration under the loose constraint of the calibrator attitude and compensate for the influence of channel inconsistency on polarization information measurement; additionally, an insect orientation estimation method based on a biological symmetry model is proposed, and the mechanism of cross-talk between polarization channels on orientation estimation is analyzed analytically. Finally, multifrequency high-resolution fully polarimetric radar (X, Ku, Ka) is used for polarimetric calibration and insect orientation measurement experiments, and the measurement error of orientation is less than 3°, which verified the feasibility and effectiveness of the proposed method. Migratory pests are sudden outbreaks and widespread, putting national food security at risk. Entomological radar is most effective segment in monitoring insect migration, providing critical information for early warning and pest control. Traditional entomological radar can measure biological parameters such as mass and orientation using a low-resolution waveform and rotating linear polarization antenna. The new entomological radar uses a stepped chirp high-resolution waveform and instantaneous fully polarimetric system, which can considerably improve the accuracy of measuring insect biological data. However, in addition to the traditional polarization measurement errors, the stepped chirp waveform introduces new multiplicative error components to different polarization channels, resulting in a more complex imbalance between polarization channels, which requires high-precision polarization calibration. In response to the above issues, the fully polarimetric measurement model is optimized according to the characteristics of the high-resolution system, and a high-resolution system polarization error estimation method based on a sphere and a wire is proposed in this study, which can complete polarization calibration under the loose constraint of the calibrator attitude and compensate for the influence of channel inconsistency on polarization information measurement; additionally, an insect orientation estimation method based on a biological symmetry model is proposed, and the mechanism of cross-talk between polarization channels on orientation estimation is analyzed analytically. Finally, multifrequency high-resolution fully polarimetric radar (X, Ku, Ka) is used for polarimetric calibration and insect orientation measurement experiments, and the measurement error of orientation is less than 3°, which verified the feasibility and effectiveness of the proposed method.
This study proposes a fast power allocation algorithm under a low interception background for a collocated MIMO radar that simultaneously tracks multiple maneuvering targets. First, the target maneuver process is modeled as an Adaptive Current Statistical (ACS) model, and a particle filter is used to estimate the state of each target. Second, the Predicted Conditional Cramer-Rao Lower Bound (PC-CRLB) is derived, and the target comprehensive threat assessment model is constructed based on the target motion and electromagnetic characteristics. Subsequently, an optimization model with respect to transmitting power is established by developing the weighted sum of the target tracking error evaluation index and the unintercepted probability of radar as the optimization objective. Thereafter, to solve the model using the monotonically decreasing property of the objective function, a solving algorithm based on sequence relaxation is proposed. Finally, a simulation is conducted to verify the effectiveness and timeliness of the proposed algorithm. The results indicate that the proposed algorithm can effectively improve the target tracking accuracy and low interception performance of the radar system. Further, its run speed is increased by nearly 50% compared with that of the interior point method. This study proposes a fast power allocation algorithm under a low interception background for a collocated MIMO radar that simultaneously tracks multiple maneuvering targets. First, the target maneuver process is modeled as an Adaptive Current Statistical (ACS) model, and a particle filter is used to estimate the state of each target. Second, the Predicted Conditional Cramer-Rao Lower Bound (PC-CRLB) is derived, and the target comprehensive threat assessment model is constructed based on the target motion and electromagnetic characteristics. Subsequently, an optimization model with respect to transmitting power is established by developing the weighted sum of the target tracking error evaluation index and the unintercepted probability of radar as the optimization objective. Thereafter, to solve the model using the monotonically decreasing property of the objective function, a solving algorithm based on sequence relaxation is proposed. Finally, a simulation is conducted to verify the effectiveness and timeliness of the proposed algorithm. The results indicate that the proposed algorithm can effectively improve the target tracking accuracy and low interception performance of the radar system. Further, its run speed is increased by nearly 50% compared with that of the interior point method.
Most traditional multi-aircraft flight path optimization methods are oriented toward area coverage, use static optimization models, and face the challenge of model mismatch under complex dynamic environments. Therefore, this study proposes a flight path optimization method for dynamic area coverage based on multi-aircraft radars. First, we introduce an attenuation factor to this method to characterize the actual coverage effect of airborne radar on a dynamic environment, and we take the area coverage rate under the dynamic area coverage background as the optimization function. After integrating the constraints of multi-dimensional flight path control parameters to be optimized, we built a mathematical model for dynamic area coverage flight path optimization based on multi-aircraft radars. Then, the stochastic optimization method is used to solve the flight path optimization problem of dynamic area coverage. Finally, the simulation results show that the proposed flight path optimization method can significantly improve the dynamic coverage performance in dynamic areas compared with the search mode using preset flight paths based on multi-aircraft radars. Compared with the traditional flight path optimization method oriented to static environments, the dynamic coverage performance of our proposed method is improved by approximately 6% on average. Most traditional multi-aircraft flight path optimization methods are oriented toward area coverage, use static optimization models, and face the challenge of model mismatch under complex dynamic environments. Therefore, this study proposes a flight path optimization method for dynamic area coverage based on multi-aircraft radars. First, we introduce an attenuation factor to this method to characterize the actual coverage effect of airborne radar on a dynamic environment, and we take the area coverage rate under the dynamic area coverage background as the optimization function. After integrating the constraints of multi-dimensional flight path control parameters to be optimized, we built a mathematical model for dynamic area coverage flight path optimization based on multi-aircraft radars. Then, the stochastic optimization method is used to solve the flight path optimization problem of dynamic area coverage. Finally, the simulation results show that the proposed flight path optimization method can significantly improve the dynamic coverage performance in dynamic areas compared with the search mode using preset flight paths based on multi-aircraft radars. Compared with the traditional flight path optimization method oriented to static environments, the dynamic coverage performance of our proposed method is improved by approximately 6% on average.
Holographic staring radar is an array radar that continuously looks everywhere and performs multiple functions simultaneously instead of sequentially. First, this paper clarifies the definition of holographic staring radar and summarizes the features, performance advantages, and accompanying risks of holographic staring radar. Then, the research history and main application directions of holographic staring radar are reviewed. Next, the holographic staring radar series of Sun Yat-sen University in China is introduced. The target detection results of this holographic staring radar are given, showing the application potential of a holographic staring radar system in low-altitude target monitoring. Next, the research progress of related key technologies is examined, including system design, beam control, target detection, and parameter estimation. Finally, the development trends of holographic staring radar are discussed. Holographic staring radar is an array radar that continuously looks everywhere and performs multiple functions simultaneously instead of sequentially. First, this paper clarifies the definition of holographic staring radar and summarizes the features, performance advantages, and accompanying risks of holographic staring radar. Then, the research history and main application directions of holographic staring radar are reviewed. Next, the holographic staring radar series of Sun Yat-sen University in China is introduced. The target detection results of this holographic staring radar are given, showing the application potential of a holographic staring radar system in low-altitude target monitoring. Next, the research progress of related key technologies is examined, including system design, beam control, target detection, and parameter estimation. Finally, the development trends of holographic staring radar are discussed.
Because Doppler resilience is limited in the existing joint design of Integrated Sensing And Communication (ISAC) waveforms, a new Doppler resilient ISAC waveform design is proposed based on a joint design. First, with the pulse train ambiguity function, a construction of the Doppler resilient pulse train is deduced, which is equivalent to designing a waveform with a very low integral sidelobe level in a correlation zone. Accordingly, to construct the Doppler resilient ISAC pulse train, an optimization problem is proposed that takes minimizing the weighted integral sidelobe level of the ISAC waveform as the objective function and takes the energy of the transmitted waveform, the peak-to-average power ratio, and the phase difference between the transmitted ISAC waveform and the communication data modulated waveform as constraints. Because the optimization problem is nonconvex, an iterative optimization algorithm based on the Majorization-Minimization (MM) framework is proposed to solve it. Nu­merical simulation experiments show that compared with the traditional ISAC waveform design method, the ISAC waveform proposed in this paper has higher Doppler resilience and a lower symbol error rate, and the detection performance of the ISAC system for moving targets is considerably improved without loss of communication quality. Because Doppler resilience is limited in the existing joint design of Integrated Sensing And Communication (ISAC) waveforms, a new Doppler resilient ISAC waveform design is proposed based on a joint design. First, with the pulse train ambiguity function, a construction of the Doppler resilient pulse train is deduced, which is equivalent to designing a waveform with a very low integral sidelobe level in a correlation zone. Accordingly, to construct the Doppler resilient ISAC pulse train, an optimization problem is proposed that takes minimizing the weighted integral sidelobe level of the ISAC waveform as the objective function and takes the energy of the transmitted waveform, the peak-to-average power ratio, and the phase difference between the transmitted ISAC waveform and the communication data modulated waveform as constraints. Because the optimization problem is nonconvex, an iterative optimization algorithm based on the Majorization-Minimization (MM) framework is proposed to solve it. Nu­merical simulation experiments show that compared with the traditional ISAC waveform design method, the ISAC waveform proposed in this paper has higher Doppler resilience and a lower symbol error rate, and the detection performance of the ISAC system for moving targets is considerably improved without loss of communication quality.
The hardware imperfection can generate a unique fingerprint of the trasmitter, and it is attached to the radio signal. The unique attribute of transmitter can be used for Radio Frequency Fingerprinting (RFF). Due to the unknown channel conditional and the lack of prior information such as modulation scheme, the traditional method of RFF faces huge challenges to non-cooperative conditions. On the contrary, RFF methods based on Deep Learning (DL), especially those that can directly process raw I/Q, show great potential. However, the research results of this direction are scattered, which seriously hinders researchers from grasping the key issues. This paper first classifies and compares the RFF methods based on DL according to the utilization of prior knowledge, and focuses on the RFF methods based on raw I/Q and DL. Then, this paper focuses on the classification and discussion of the deep neural network model of RFF using raw I/Q, and summarizes the open source data sets, data representation methods and data augmentation methods related to RFF. Finally, this paper discusses the difficulties and research directions of the RFF based on DL, hoping to help the research and application of the RFF. The hardware imperfection can generate a unique fingerprint of the trasmitter, and it is attached to the radio signal. The unique attribute of transmitter can be used for Radio Frequency Fingerprinting (RFF). Due to the unknown channel conditional and the lack of prior information such as modulation scheme, the traditional method of RFF faces huge challenges to non-cooperative conditions. On the contrary, RFF methods based on Deep Learning (DL), especially those that can directly process raw I/Q, show great potential. However, the research results of this direction are scattered, which seriously hinders researchers from grasping the key issues. This paper first classifies and compares the RFF methods based on DL according to the utilization of prior knowledge, and focuses on the RFF methods based on raw I/Q and DL. Then, this paper focuses on the classification and discussion of the deep neural network model of RFF using raw I/Q, and summarizes the open source data sets, data representation methods and data augmentation methods related to RFF. Finally, this paper discusses the difficulties and research directions of the RFF based on DL, hoping to help the research and application of the RFF.
Facing the increasingly complex electromagnetic interference environment, Synthetic Aperture Radar (SAR) interference suppression has become an urgent problem to be solved. The existing mainstream synthetic aperture radar nonparametric/parametric interference suppression methods, which heavily rely on interference priori and strong energy difference, have serious problems such as high computational complexity and signal loss, and have difficulty in meeting the needs of countering increasingly complex interference. To solve the aforementioned problems, we propose an anti-interference method using self-supervised learning based on deep learning, which uses the time-frequency domain texture difference between normal radar echo and interference to overcome the constraint of using interference prior. First, we construct an interference location network model Location-Net, which compresses and reconstructs the time-frequency spectrum of the radar echo and locates the interference according to the network’s reconstruction error. Second, aiming at the signal loss caused by interference suppression, a signal recovery neural network model Recovery-Net is constructed to recover the echo signal after interference suppression. Compared with traditional methods, our method overcomes the need for interference prior, can effectively resist various complex interference types and has strong generalization ability. The anti-interference processing results based on simulation and measured data verify the effectiveness of the proposed method for various active main lobe suppression interference and show the superiority of the algorithm proposed here by comparing it with three existing anti-interference methods. Finally, comparing the complexity difference between the proposed and mainstream lightweight neural networks shows that the neural networks designed here have low computational complexity and real-time application prospects. Facing the increasingly complex electromagnetic interference environment, Synthetic Aperture Radar (SAR) interference suppression has become an urgent problem to be solved. The existing mainstream synthetic aperture radar nonparametric/parametric interference suppression methods, which heavily rely on interference priori and strong energy difference, have serious problems such as high computational complexity and signal loss, and have difficulty in meeting the needs of countering increasingly complex interference. To solve the aforementioned problems, we propose an anti-interference method using self-supervised learning based on deep learning, which uses the time-frequency domain texture difference between normal radar echo and interference to overcome the constraint of using interference prior. First, we construct an interference location network model Location-Net, which compresses and reconstructs the time-frequency spectrum of the radar echo and locates the interference according to the network’s reconstruction error. Second, aiming at the signal loss caused by interference suppression, a signal recovery neural network model Recovery-Net is constructed to recover the echo signal after interference suppression. Compared with traditional methods, our method overcomes the need for interference prior, can effectively resist various complex interference types and has strong generalization ability. The anti-interference processing results based on simulation and measured data verify the effectiveness of the proposed method for various active main lobe suppression interference and show the superiority of the algorithm proposed here by comparing it with three existing anti-interference methods. Finally, comparing the complexity difference between the proposed and mainstream lightweight neural networks shows that the neural networks designed here have low computational complexity and real-time application prospects.
Compared with narrowband Doppler radar, ultrawideband radar can simultaneously acquire the range and Doppler information of targets, which is more beneficial for behavior recognition. To improve the recognition performance of fall behavior, frequency-modulated continuous-wave ultrawideband (UWB) radar was applied to collect daily behavior and fall data of 36 subjects in two real indoor complex scenes, and a multi-scene fall detection dataset was established with various action types; the range-time, time-Doppler, and range-Doppler spectrograms of the subjects were obtained after preprocessing radar data; based on the MobileNet-V3 lightweight network, three types of deep learning fusion networks at the data level, feature level, and decision level were designed for the radar spectrograms, respectively. A statistical analysis shows that the decision level fusion method proposed in this paper can improve fall detection performance compared with those using one type of spectrogram, the data level and the feature level fusion methods (all P values by significance test method are less than 0.003). The accuracies of 5-fold cross-validation and testing in the new scene of the decision level fusion method are 0.9956 and 0.9778, respectively, which indicates the good generalization ability of the proposed method. Compared with narrowband Doppler radar, ultrawideband radar can simultaneously acquire the range and Doppler information of targets, which is more beneficial for behavior recognition. To improve the recognition performance of fall behavior, frequency-modulated continuous-wave ultrawideband (UWB) radar was applied to collect daily behavior and fall data of 36 subjects in two real indoor complex scenes, and a multi-scene fall detection dataset was established with various action types; the range-time, time-Doppler, and range-Doppler spectrograms of the subjects were obtained after preprocessing radar data; based on the MobileNet-V3 lightweight network, three types of deep learning fusion networks at the data level, feature level, and decision level were designed for the radar spectrograms, respectively. A statistical analysis shows that the decision level fusion method proposed in this paper can improve fall detection performance compared with those using one type of spectrogram, the data level and the feature level fusion methods (all P values by significance test method are less than 0.003). The accuracies of 5-fold cross-validation and testing in the new scene of the decision level fusion method are 0.9956 and 0.9778, respectively, which indicates the good generalization ability of the proposed method.
For the resource allocation problem of multitarget tracking in a spectral coexistence environment, this study proposes a joint transmit power and dwell time allocation algorithm for radar networks. First, the predicted Bayesian Cramér-Rao Lower Bound (BCRLB) with the variables of radar node selection, transmit power and dwell time is derived as the performance metric for multi-target tracking accuracy. On this basis, a joint optimization model of transmit power and dwell time allocation for multitarget tracking in radar networks under spectral coexistence is built to collaboratively optimize the radar node selection, transmit power and dwell time of radar networks, This joint optimization model aims to minimize the multitarget tracking BCRLB while satisfying the given transmit resources of radar networks and the predetermined maximum allowable interference energy threshold of the communication base station. Subsequently, for the aforementioned optimization problem, a two-step decomposition method is used to decompose it into multiple subconvex problems, which are solved by combining the Semi-Definite Programming (SDP) and cyclic minimization algorithms. The simulation results showed that, compared with the existing algorithms, the proposed algorithm can effectively improve the multitarget tracking accuracy of radar networks while ensuring that the communication base station works properly. For the resource allocation problem of multitarget tracking in a spectral coexistence environment, this study proposes a joint transmit power and dwell time allocation algorithm for radar networks. First, the predicted Bayesian Cramér-Rao Lower Bound (BCRLB) with the variables of radar node selection, transmit power and dwell time is derived as the performance metric for multi-target tracking accuracy. On this basis, a joint optimization model of transmit power and dwell time allocation for multitarget tracking in radar networks under spectral coexistence is built to collaboratively optimize the radar node selection, transmit power and dwell time of radar networks, This joint optimization model aims to minimize the multitarget tracking BCRLB while satisfying the given transmit resources of radar networks and the predetermined maximum allowable interference energy threshold of the communication base station. Subsequently, for the aforementioned optimization problem, a two-step decomposition method is used to decompose it into multiple subconvex problems, which are solved by combining the Semi-Definite Programming (SDP) and cyclic minimization algorithms. The simulation results showed that, compared with the existing algorithms, the proposed algorithm can effectively improve the multitarget tracking accuracy of radar networks while ensuring that the communication base station works properly.
Using Depth Neural Network (DNN) modeling technology, a prediction model of Doppler spectral parameters of sea clutter based on multiple measurement conditions is established based on measured data of sea clutter from shore-based radar under different radar parameters and marine environmental parameters. The recognition of sea clutter spectral characteristics based on environmental characteristics and independent of clutter data is realized. The spectral frequency shift and broadening prediction accuracy are greater than 90%. Based on the prediction model, an analysis method of Doppler spectrum influence factors based on the parameter cycle decreasing cognition is proposed. The influence of different measurement parameters on the Doppler spectrum prediction of sea clutter is analyzed, and the change law of spectrum parameters with the main influence factors is obtained. The results are of great significance to the application of sea surface target detection based on Doppler characteristics. Using Depth Neural Network (DNN) modeling technology, a prediction model of Doppler spectral parameters of sea clutter based on multiple measurement conditions is established based on measured data of sea clutter from shore-based radar under different radar parameters and marine environmental parameters. The recognition of sea clutter spectral characteristics based on environmental characteristics and independent of clutter data is realized. The spectral frequency shift and broadening prediction accuracy are greater than 90%. Based on the prediction model, an analysis method of Doppler spectrum influence factors based on the parameter cycle decreasing cognition is proposed. The influence of different measurement parameters on the Doppler spectrum prediction of sea clutter is analyzed, and the change law of spectrum parameters with the main influence factors is obtained. The results are of great significance to the application of sea surface target detection based on Doppler characteristics.
This paper establishes a hybrid distributed Phased-Array Multiple-Input Multiple-Output (PA-MIMO) radar system model, which combines coherent processing gain and spatial diversity gain to synergistically improve the target detection performance. We derive a Likelihood Ratio Test (LRT) detector based on the Neyman-Pearson (NP) criterion for the hybrid distributed PA-MIMO radar system. The coherent processing gain and spatial diversity gain are jointly optimized by implementing subarray-level and array element–level optimal configurations at the transceiver and transmitter ends. Moreover, a Quantum Particle Swarm Optimization-based Stochastic Rounding (SR-QPSO) algorithm is proposed for the integer programming-based configuration model. This algorithm ensures that the optimal array-element configuration strategy is obtained with less iteration and achieves the joint optimization of subarray and array-element levels. Finally, simulations verify that the proposed optimal configuration offers substantial improvements compared to other typical radar systems, with a detection probability of 0.98 and an effective range of 1166.3 km, as well as a considerably improved detection performance. This paper establishes a hybrid distributed Phased-Array Multiple-Input Multiple-Output (PA-MIMO) radar system model, which combines coherent processing gain and spatial diversity gain to synergistically improve the target detection performance. We derive a Likelihood Ratio Test (LRT) detector based on the Neyman-Pearson (NP) criterion for the hybrid distributed PA-MIMO radar system. The coherent processing gain and spatial diversity gain are jointly optimized by implementing subarray-level and array element–level optimal configurations at the transceiver and transmitter ends. Moreover, a Quantum Particle Swarm Optimization-based Stochastic Rounding (SR-QPSO) algorithm is proposed for the integer programming-based configuration model. This algorithm ensures that the optimal array-element configuration strategy is obtained with less iteration and achieves the joint optimization of subarray and array-element levels. Finally, simulations verify that the proposed optimal configuration offers substantial improvements compared to other typical radar systems, with a detection probability of 0.98 and an effective range of 1166.3 km, as well as a considerably improved detection performance.
To reduce the probability of UAV (Unmanned Aerial Vehicle) being destroyed during a reconnaissance mission, this study proposes an effective path planning algorithm to reduce the target threat. First, high-resolution airborne radar is used for robust tracking and estimation of multiple extended targets. Subsequently, the targets are classified based on the threat degree calculated via fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). Next, path planning of a UAV is performed considering joint optimization of multiple task decision-making (the joint evaluation of the target threat degree and target tracking performance) as an evaluation criterion. The simulation results indicate that the fuzzy threat assessment method is effective in multiple extended target tracking, and the proposed UAV path planning algorithm is reasonable. Thus the target threat is efficiently reduced without losing the tracking accuracy. To reduce the probability of UAV (Unmanned Aerial Vehicle) being destroyed during a reconnaissance mission, this study proposes an effective path planning algorithm to reduce the target threat. First, high-resolution airborne radar is used for robust tracking and estimation of multiple extended targets. Subsequently, the targets are classified based on the threat degree calculated via fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). Next, path planning of a UAV is performed considering joint optimization of multiple task decision-making (the joint evaluation of the target threat degree and target tracking performance) as an evaluation criterion. The simulation results indicate that the fuzzy threat assessment method is effective in multiple extended target tracking, and the proposed UAV path planning algorithm is reasonable. Thus the target threat is efficiently reduced without losing the tracking accuracy.
Atmospheric influence is the main interference factor in Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) deformation monitoring. Due to the complex terrain and various environmental factors, the correction method based on the assumption of a uniform atmospheric influence may lead to low atmospheric correction accuracy. In this paper, a two-stage semi-empirical model is proposed to correct the atmospheric phase screen during the GB-InSAR monitoring of a super large slope under complex atmospheric conditions. First, the observed atmospheric phase is modeled according to the height and range of the terrain structure to correct the linear atmospheric phase. Then, considering the complex atmospheric conditions and the spatially nonuniform atmosphere with a large azimuth field of view, stable Persistent Scatterers (PS) are selected to obtain the atmospheric phase of all PS by interpolation to correct the nonlinear atmospheric phase. This method is used to process a large field of view radar image of the foundation of the Xinpu and Outang landslides in the Three Gorges Reservoir area. Compared with the conventional method, the atmospheric phase error is reduced by approximately 2 mm. This method effectively corrects the nonuniform atmospheric phase under the landslide monitoring scene and meets the wide-area monitoring needs of the landslide. Atmospheric influence is the main interference factor in Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) deformation monitoring. Due to the complex terrain and various environmental factors, the correction method based on the assumption of a uniform atmospheric influence may lead to low atmospheric correction accuracy. In this paper, a two-stage semi-empirical model is proposed to correct the atmospheric phase screen during the GB-InSAR monitoring of a super large slope under complex atmospheric conditions. First, the observed atmospheric phase is modeled according to the height and range of the terrain structure to correct the linear atmospheric phase. Then, considering the complex atmospheric conditions and the spatially nonuniform atmosphere with a large azimuth field of view, stable Persistent Scatterers (PS) are selected to obtain the atmospheric phase of all PS by interpolation to correct the nonlinear atmospheric phase. This method is used to process a large field of view radar image of the foundation of the Xinpu and Outang landslides in the Three Gorges Reservoir area. Compared with the conventional method, the atmospheric phase error is reduced by approximately 2 mm. This method effectively corrects the nonuniform atmospheric phase under the landslide monitoring scene and meets the wide-area monitoring needs of the landslide.
The interrupted-sampling repeater jammer can sample, store, process and transmit part of the radar transmitter signal multiple times and the fake targets will form on the radar receiver. To improve the radar performance in the aforementioned jamming scenario, in this study, a new signal differential feature extraction method is proposed, and a judgment criterion is formulated based on the difference between the target echo and the Interrupted-Sampling Repeater Jamming (ISRJ) in the differential feature space to effectively identify and suppress the ISRJ while achieving target detection. Simulation results show that the proposed method has a remarkable ISRJ suppression performance. The equivalent signal-to-noise ratio is improved by at least 4.2 dB compared with three typical time-frequency domain filtering algorithms. The interrupted-sampling repeater jammer can sample, store, process and transmit part of the radar transmitter signal multiple times and the fake targets will form on the radar receiver. To improve the radar performance in the aforementioned jamming scenario, in this study, a new signal differential feature extraction method is proposed, and a judgment criterion is formulated based on the difference between the target echo and the Interrupted-Sampling Repeater Jamming (ISRJ) in the differential feature space to effectively identify and suppress the ISRJ while achieving target detection. Simulation results show that the proposed method has a remarkable ISRJ suppression performance. The equivalent signal-to-noise ratio is improved by at least 4.2 dB compared with three typical time-frequency domain filtering algorithms.
Synthetic Aperture Radar (SAR) image ship target detection has attracted considerable attention. As a state-of-the-art method, the Constant False Alarm Rate (CFAR) detection algorithm is often used in SAR image ship target detection. However, the detection performance of the classical CFAR is easily affected by speckle noise. Moreover, the detection results based on the sliding window are sensitive to the size of the sliding window. Thus, ensuring that there are no target pixels in the cluttered background is difficult, which easily leads to a high computational load. This study proposes a new ship target detection method for SAR images based on fast superpixel-based non-window CFAR to solve these problems. The superpixel generation method of Density-Based Spatial Clustering of Applications with Noise is used to generate superpixels for SAR images. Under the assumption that SAR data obey the Rayleigh mixture distribution, we define a superpixel dissimilarity measure. Then, the clutter parameters of each pixel are accurately estimated using superpixels, which can avoid the shortcomings of the traditional CFAR sliding window even in the case of multiple targets. A local contrast based on the Coefficient of Variation (CoV) of the SAR image is proposed to optimize the CFAR detection result, which can eliminate a large number of false alarms from man-made targets in urban areas. The experimental results of five real SAR images show that the proposed method for ship target detection in SAR images with different scenes is robust compared with other state-of-the-art methods. Synthetic Aperture Radar (SAR) image ship target detection has attracted considerable attention. As a state-of-the-art method, the Constant False Alarm Rate (CFAR) detection algorithm is often used in SAR image ship target detection. However, the detection performance of the classical CFAR is easily affected by speckle noise. Moreover, the detection results based on the sliding window are sensitive to the size of the sliding window. Thus, ensuring that there are no target pixels in the cluttered background is difficult, which easily leads to a high computational load. This study proposes a new ship target detection method for SAR images based on fast superpixel-based non-window CFAR to solve these problems. The superpixel generation method of Density-Based Spatial Clustering of Applications with Noise is used to generate superpixels for SAR images. Under the assumption that SAR data obey the Rayleigh mixture distribution, we define a superpixel dissimilarity measure. Then, the clutter parameters of each pixel are accurately estimated using superpixels, which can avoid the shortcomings of the traditional CFAR sliding window even in the case of multiple targets. A local contrast based on the Coefficient of Variation (CoV) of the SAR image is proposed to optimize the CFAR detection result, which can eliminate a large number of false alarms from man-made targets in urban areas. The experimental results of five real SAR images show that the proposed method for ship target detection in SAR images with different scenes is robust compared with other state-of-the-art methods.
In the Synthetic Aperture Radar (SAR) ship target detection task, the targets have a large aspect ratio and dense distribution, and they are arranged in arbitrary directions. The oriented bounding box-based detection methods can output accurate detection results. However, these methods are strongly restricted by high computational complexity, slow inference speed, and large storage consumption, which complicate their deployment on space-borne platforms. To solve the above issues, a lightweight oriented anchor-free-based detection method is proposed by combining feature map and prediction head knowledge distillation. First, we propose an improved Gaussian kernel based on the aspect ratio and angle information so that the generated heatmaps can better describe the shape of the targets. Second, the foreground region enhancement branch is introduced to make the network focus more on foreground features while suppressing the background interference. When training the lightweight student network, the similarity between pixels is treated as transferred knowledge in heatmap distillation. To tackle the imbalance between positive and negative samples in feature distillation, the foreground attention region is applied as a mask to guide the feature distillation process. In addition, a global semantic module is proposed to model the contextual information around pixels, and the background knowledge is combined to further strengthen the feature representation. Experimental results based on HRSID show that our method can achieve 80.71% mAP with only 9.07 M model parameters, and the detection frame rate meets the needs of real-time applications. In the Synthetic Aperture Radar (SAR) ship target detection task, the targets have a large aspect ratio and dense distribution, and they are arranged in arbitrary directions. The oriented bounding box-based detection methods can output accurate detection results. However, these methods are strongly restricted by high computational complexity, slow inference speed, and large storage consumption, which complicate their deployment on space-borne platforms. To solve the above issues, a lightweight oriented anchor-free-based detection method is proposed by combining feature map and prediction head knowledge distillation. First, we propose an improved Gaussian kernel based on the aspect ratio and angle information so that the generated heatmaps can better describe the shape of the targets. Second, the foreground region enhancement branch is introduced to make the network focus more on foreground features while suppressing the background interference. When training the lightweight student network, the similarity between pixels is treated as transferred knowledge in heatmap distillation. To tackle the imbalance between positive and negative samples in feature distillation, the foreground attention region is applied as a mask to guide the feature distillation process. In addition, a global semantic module is proposed to model the contextual information around pixels, and the background knowledge is combined to further strengthen the feature representation. Experimental results based on HRSID show that our method can achieve 80.71% mAP with only 9.07 M model parameters, and the detection frame rate meets the needs of real-time applications.
This paper studies adaptive distributed targets detection for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, where the targets are embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model considers also the distributed targets establishing as a summation expression, which is different from the classic detection models in MIMO and/or phase array radars that discuss only point-like targets. Next, the detector through Rao criterion without the need of training data are proposed. The proposed method together with all theoretical analysis are verified by numerical results. This paper studies adaptive distributed targets detection for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, where the targets are embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model considers also the distributed targets establishing as a summation expression, which is different from the classic detection models in MIMO and/or phase array radars that discuss only point-like targets. Next, the detector through Rao criterion without the need of training data are proposed. The proposed method together with all theoretical analysis are verified by numerical results.
Dense-repeated jamming is highly related to the radar-transmitted signal, and it has suppression and deception jamming effects, which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar. To solve this problem, an intelligent suppression method based on the Support Vector Machine (SVM) is proposed in this paper. The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference. Then, the interference sidelobe in the target range unit is further suppressed by smoothing filtering. Finally, high-resolution two-dimensional reconstruction is performed based on compress sensing theory to estimate the target parameter information. Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios. Dense-repeated jamming is highly related to the radar-transmitted signal, and it has suppression and deception jamming effects, which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar. To solve this problem, an intelligent suppression method based on the Support Vector Machine (SVM) is proposed in this paper. The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference. Then, the interference sidelobe in the target range unit is further suppressed by smoothing filtering. Finally, high-resolution two-dimensional reconstruction is performed based on compress sensing theory to estimate the target parameter information. Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios.
Due to several advantages of the Multi-Input Multi-Output (MIMO) system in terms of waveform, space diversity, and multiplexing, the MIMO Dual Function Radar and Communication (DFRC) system, which is responsible for target detection and securing the communication by sharing the software and hardware resources, has attracted great attention. This paper addresses the MIMO DFRC system based on permutation matrix modulation and proposes a DFRC signal matrix design method based on the Alternation Direction Method of Multipliers (ADMM). By maximizing the Peak Mainlobe to Sidelobe level Ratio (PMSR) of the beampattern with the constraints of the reference codebook for both users and eavesdroppers, the system guarantees excellent detection performance along with protecting the communication information from interception. Aiming at the communication demodulation of the permutation matrix, a permutation learning demodulation method based on the Alternating Direction Penalty Method (ADPM) is proposed to improve the demodulation efficiency of the co-use waveform. Numerical simulations verify the effectiveness of the proposed methods to achieve dual function, capable of realizing multiuser communication and deriving higher PMSR compared with the existing counterparts. Due to several advantages of the Multi-Input Multi-Output (MIMO) system in terms of waveform, space diversity, and multiplexing, the MIMO Dual Function Radar and Communication (DFRC) system, which is responsible for target detection and securing the communication by sharing the software and hardware resources, has attracted great attention. This paper addresses the MIMO DFRC system based on permutation matrix modulation and proposes a DFRC signal matrix design method based on the Alternation Direction Method of Multipliers (ADMM). By maximizing the Peak Mainlobe to Sidelobe level Ratio (PMSR) of the beampattern with the constraints of the reference codebook for both users and eavesdroppers, the system guarantees excellent detection performance along with protecting the communication information from interception. Aiming at the communication demodulation of the permutation matrix, a permutation learning demodulation method based on the Alternating Direction Penalty Method (ADPM) is proposed to improve the demodulation efficiency of the co-use waveform. Numerical simulations verify the effectiveness of the proposed methods to achieve dual function, capable of realizing multiuser communication and deriving higher PMSR compared with the existing counterparts.
Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed. Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed.
The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency. Sparse SAR imaging has grown in importance as a research branch in SAR imaging due to the high potential of sparse signal processing techniques based on Compressed Sensing (CS) to show high potential in reducing system complexity and improving imaging quality. However, traditional sparse imaging methods are still constrained by high computational complexity, nontrivial parameter tuning, and poor adaptability to weakly sparse scenes. To address these issues, we propose a new 3D SAR imaging method based on learned sparse priors inspired by the deep unfolding concept. First, the limitations of the matrix-vector linear representation model are discussed, and an imaging operator is introduced to improve the algorithm's imaging efficiency. Furthermore, this research focuses on algorithm network details, such as network topology design, the problem of complex-valued propagations, optimization constraints of algorithm parameters, and network training details. Finally, through simulations and measured experiments, it is proved that the proposed method can improve the imaging accuracy while reducing the running time by more than one order of magnitude compared with the conventional sparse imaging algorithms. The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency. Sparse SAR imaging has grown in importance as a research branch in SAR imaging due to the high potential of sparse signal processing techniques based on Compressed Sensing (CS) to show high potential in reducing system complexity and improving imaging quality. However, traditional sparse imaging methods are still constrained by high computational complexity, nontrivial parameter tuning, and poor adaptability to weakly sparse scenes. To address these issues, we propose a new 3D SAR imaging method based on learned sparse priors inspired by the deep unfolding concept. First, the limitations of the matrix-vector linear representation model are discussed, and an imaging operator is introduced to improve the algorithm's imaging efficiency. Furthermore, this research focuses on algorithm network details, such as network topology design, the problem of complex-valued propagations, optimization constraints of algorithm parameters, and network training details. Finally, through simulations and measured experiments, it is proved that the proposed method can improve the imaging accuracy while reducing the running time by more than one order of magnitude compared with the conventional sparse imaging algorithms.
Cyclic prefixes in joint radar and communication systems based on Orthogonal Frequency Division Multiplexing (OFDM) and low probability of interception lead to weak radar echo masking on the battlefield. To address this problem, a low probability of interception waveform design scheme based on Filter Bank Multi-Carrier (FBMC) with Offset Quadrature Amplitude Modulation (FBMC-OQAM) is proposed in this paper. Mathematical models for the FBMC joint radar and communication waveform, target detection probability, and communication channel capacity are established. Under the radar and communication performance constraints required by the system, a joint optimization problem of minimizing the total transmitted power of the system is designed, and the subcarrier and power allocation scheme are optimized. Furthermore, the proposed algorithm can realize adaptive transmission where the parameters of the transmitting waveform can be optimally designed for the next pulse by utilizing the measured values of the current signal and the channel state information. Moreover, the feasibility and advantages of FBMC as the radar signal are analyzed based on the average ambiguity function. Theoretical analysis and simulation experiments show that the power allocation scheme proposed in this paper can effectively reduce the total transmitted power of the system, to achieve low interception performance compared with the equal power allocation. The FBMC waveform can effectively reduce the sidelobes caused by cyclic prefixes, which improves the radar resolution and information rate. Cyclic prefixes in joint radar and communication systems based on Orthogonal Frequency Division Multiplexing (OFDM) and low probability of interception lead to weak radar echo masking on the battlefield. To address this problem, a low probability of interception waveform design scheme based on Filter Bank Multi-Carrier (FBMC) with Offset Quadrature Amplitude Modulation (FBMC-OQAM) is proposed in this paper. Mathematical models for the FBMC joint radar and communication waveform, target detection probability, and communication channel capacity are established. Under the radar and communication performance constraints required by the system, a joint optimization problem of minimizing the total transmitted power of the system is designed, and the subcarrier and power allocation scheme are optimized. Furthermore, the proposed algorithm can realize adaptive transmission where the parameters of the transmitting waveform can be optimally designed for the next pulse by utilizing the measured values of the current signal and the channel state information. Moreover, the feasibility and advantages of FBMC as the radar signal are analyzed based on the average ambiguity function. Theoretical analysis and simulation experiments show that the power allocation scheme proposed in this paper can effectively reduce the total transmitted power of the system, to achieve low interception performance compared with the equal power allocation. The FBMC waveform can effectively reduce the sidelobes caused by cyclic prefixes, which improves the radar resolution and information rate.