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Editor Work2024 Vol. 13, No. 1
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2024, 13(1): 1-22.
Abstract
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The Back Projection (BP) algorithm is an important direction in the development of synthetic aperture radar imaging algorithms. However, the large computational load of the BP algorithm has hindered its development in engineering applications. Therefore, techniques to enhance the computational efficiency of the BP algorithm have recently received widespread attention. This paper discusses the fast BP algorithm based on various imaging plane coordinate systems, including the distance-azimuth plane coordinate system, the ground plane coordinate system, and the non-Euclidean coordinate system. First, the principle of the original BP algorithm and the impact of different coordinate systems on accelerating the BP algorithm are introduced, and the development history of the BP algorithm is sorted out. Then, the research progress of the fast BP algorithm based on different imaging plane coordinate systems is examined, focusing on the recent research work completed by the author’s research team. Finally, the application of fast BP algorithm in engineering is introduced, and the research development trend of the fast BP imaging algorithm is discussed.
The Back Projection (BP) algorithm is an important direction in the development of synthetic aperture radar imaging algorithms. However, the large computational load of the BP algorithm has hindered its development in engineering applications. Therefore, techniques to enhance the computational efficiency of the BP algorithm have recently received widespread attention. This paper discusses the fast BP algorithm based on various imaging plane coordinate systems, including the distance-azimuth plane coordinate system, the ground plane coordinate system, and the non-Euclidean coordinate system. First, the principle of the original BP algorithm and the impact of different coordinate systems on accelerating the BP algorithm are introduced, and the development history of the BP algorithm is sorted out. Then, the research progress of the fast BP algorithm based on different imaging plane coordinate systems is examined, focusing on the recent research work completed by the author’s research team. Finally, the application of fast BP algorithm in engineering is introduced, and the research development trend of the fast BP imaging algorithm is discussed.
2024, 13(1): 23-45.
The Multipath Exploitation Radar (MER) target detection technology is primarily based on the Non-Line-Of-Sight (NLOS) multipath propagation characteristics of electromagnetic waves, such as reflection and diffraction on the surface of the medium, enabling the effective detection of targets hidden in the “visually” blind area, such as urban street corners and vehicle occlusion. Thus, the technology can be feasible for various applications, including urban combat and intelligent driving. Further, it has significant practical and research implications. This paper summarizes the domestic and foreign literature in this field since the beginning of the 21st century to keep abreast of developments in this field and predict future development trends. The literature review revealed that according to the different types of detection platforms, MER target detection technology primarily consists of multipath detection technologies based on air and ground platforms. Both these technologies have achieved certain produced research results of practical significance. For air platforms, the following aspects are discussed: feasibility verification, analysis of influencing factors, architectural environment perception, and NLOS target detection. Further, for ground platforms, these four aspects are covered: target detection and recognition, two-dimensional target positioning, three-dimensional target information acquisition, and new detection methods. Finally, the prospects of MER target detection technology are summarized, and the potential issues and challenges in the current practical application of this technology are highlighted. These results show that MER target detection technology is evolving toward diversification and intelligence.
The Multipath Exploitation Radar (MER) target detection technology is primarily based on the Non-Line-Of-Sight (NLOS) multipath propagation characteristics of electromagnetic waves, such as reflection and diffraction on the surface of the medium, enabling the effective detection of targets hidden in the “visually” blind area, such as urban street corners and vehicle occlusion. Thus, the technology can be feasible for various applications, including urban combat and intelligent driving. Further, it has significant practical and research implications. This paper summarizes the domestic and foreign literature in this field since the beginning of the 21st century to keep abreast of developments in this field and predict future development trends. The literature review revealed that according to the different types of detection platforms, MER target detection technology primarily consists of multipath detection technologies based on air and ground platforms. Both these technologies have achieved certain produced research results of practical significance. For air platforms, the following aspects are discussed: feasibility verification, analysis of influencing factors, architectural environment perception, and NLOS target detection. Further, for ground platforms, these four aspects are covered: target detection and recognition, two-dimensional target positioning, three-dimensional target information acquisition, and new detection methods. Finally, the prospects of MER target detection technology are summarized, and the potential issues and challenges in the current practical application of this technology are highlighted. These results show that MER target detection technology is evolving toward diversification and intelligence.
2024, 13(1): 46-67.
With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.
With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.
2024, 13(1): 68-86.
Through-the-wall radar can penetrate walls and realize indoor human target detection. Deep learning is commonly used to extract the micro-Doppler signature of a target, which can be used to effectively identify human activities behind obstacles. However, the test accuracy of the deep-learning-based recognition methods is low with poor generalization ability when different testers are invited to generate the training set and test set. Therefore, this study proposes a method for recognition of anomalous human gait termination based on micro-Doppler corner features and Non-Local mechanism. In this method, Harris and Moravec detectors are utilized to extract the corner features of the radar image, and the corner feature dataset is established in this manner. Thereafter, multilink parallel convolutions and the Non-Local mechanism are utilized to construct the global contextual information extraction network to learn the global distribution characteristics of the image pixels. The semantic feature maps are generated by repeating four times the global contextual information extraction network. Finally, the probabilities of human activities are predicted using a multilayer perceptron. The numerical simulation and experimental results demonstrate that the proposed method can effectively identify such abnormal gait termination activities as sitting, lying down, and falling, among others, which occur in the process of indoor human walking, and successfully control the generalization accuracy error to be no more than \begin{document}$ 6.4\% $\end{document} ![]()
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2024, 13(1): 87-96.
This paper proposes a novel multimodal collaborative perception framework to enhance the situational awareness of autonomous vehicles. First, a multimodal fusion baseline system is built that effectively integrates Light Detection and Ranging (LiDAR) point clouds and camera images. This system provides a comparable benchmark for subsequent research. Second, various well-known feature fusion strategies are investigated in the context of collaborative scenarios, including channel-wise concatenation, element-wise summation, and transformer-based methods. This study aims to seamlessly integrate intermediate representations from different sensor modalities, facilitating an exhaustive assessment of their effects on model performance. Extensive experiments were conducted on a large-scale open-source simulation dataset, i.e., OPV2V. The results showed that attention-based multimodal fusion outperforms alternative solutions, delivering more precise target localization during complex traffic scenarios, thereby enhancing the safety and reliability of autonomous driving systems.
This paper proposes a novel multimodal collaborative perception framework to enhance the situational awareness of autonomous vehicles. First, a multimodal fusion baseline system is built that effectively integrates Light Detection and Ranging (LiDAR) point clouds and camera images. This system provides a comparable benchmark for subsequent research. Second, various well-known feature fusion strategies are investigated in the context of collaborative scenarios, including channel-wise concatenation, element-wise summation, and transformer-based methods. This study aims to seamlessly integrate intermediate representations from different sensor modalities, facilitating an exhaustive assessment of their effects on model performance. Extensive experiments were conducted on a large-scale open-source simulation dataset, i.e., OPV2V. The results showed that attention-based multimodal fusion outperforms alternative solutions, delivering more precise target localization during complex traffic scenarios, thereby enhancing the safety and reliability of autonomous driving systems.
2024, 13(1): 97-115.
Interferometric Synthetic Aperture Radar (InSAR) enables the efficient retrieval of surface elevation and has extensive applications in terrain mapping. Dual/multi-channel InSAR techniques utilize the differences in the elevation ambiguity of different InSAR channels (i.e., baselines and frequencies) to perform Phase Unwrapping (PU). This enables the effective application of InSAR in regions with abrupt terrain changes. In response to the growing demand for efficient and precise PU, this study leverages deep learning and proposes a dual/multi-channel joint PU network, i.e., Multi-Channel-Joint-UNet (MCJ-UNet), which effectively combines multi-channel phase characteristics and their mutual constraint relationships. The proposed network is constructed based on the dual-channel (i.e., dual-frequency and dual-baseline) InSAR observation configuration. It can also be extended to multi-channel InSAR. The core concept of the proposed method can be summarized as follows. First, the method transforms the elevation ambiguity estimation problem in PU into semantic segmentation, and the UNet network is employed to accomplish the segmentation processing. Second, the squeeze-and-excitation module is introduced to dynamically adjust the information weights, enhancing the network’s perception of the required information across different channels. Third, a phase residual optimization loss function is employed in the context of multi-channel joint constraints to achieve network tuning. In addition, to mitigate the effect of edge detail errors in semantic segmentation results on PU performance, a self-correcting approach for PU errors based on multi-channel joint constraints is proposed. The proposed MCJ-UNet is verified by computer simulations based on simulated and real terrains and experiments based on real TerraSAR-X data.
Interferometric Synthetic Aperture Radar (InSAR) enables the efficient retrieval of surface elevation and has extensive applications in terrain mapping. Dual/multi-channel InSAR techniques utilize the differences in the elevation ambiguity of different InSAR channels (i.e., baselines and frequencies) to perform Phase Unwrapping (PU). This enables the effective application of InSAR in regions with abrupt terrain changes. In response to the growing demand for efficient and precise PU, this study leverages deep learning and proposes a dual/multi-channel joint PU network, i.e., Multi-Channel-Joint-UNet (MCJ-UNet), which effectively combines multi-channel phase characteristics and their mutual constraint relationships. The proposed network is constructed based on the dual-channel (i.e., dual-frequency and dual-baseline) InSAR observation configuration. It can also be extended to multi-channel InSAR. The core concept of the proposed method can be summarized as follows. First, the method transforms the elevation ambiguity estimation problem in PU into semantic segmentation, and the UNet network is employed to accomplish the segmentation processing. Second, the squeeze-and-excitation module is introduced to dynamically adjust the information weights, enhancing the network’s perception of the required information across different channels. Third, a phase residual optimization loss function is employed in the context of multi-channel joint constraints to achieve network tuning. In addition, to mitigate the effect of edge detail errors in semantic segmentation results on PU performance, a self-correcting approach for PU errors based on multi-channel joint constraints is proposed. The proposed MCJ-UNet is verified by computer simulations based on simulated and real terrains and experiments based on real TerraSAR-X data.
2024, 13(1): 116-133.
Miniaturized and lightweight Unmanned Aerial Vehicles (UAV) provide a flexible platform for Synthetic Aperture Radar (SAR). The application of UAV Interferometric SAR (InSAR) is gradually increasing in interferometric measurement fields. UAVs are small and light, which are easily affected by airflow disturbances. Their trajectories are nonlinear and unparallel when adopting the multipass mode for interferometry. The nonlinear and unparallel trajectories result in geometric distortion between the interferometric image pairs. Under complex topography conditions, the interferometric image pairs of UAVs have large offsets that are obviously space-dependent, thereby resulting in substantial technical challenges during image registration. Conventional image registration methods based on polynomial fitting are no longer applicable. In this study, we proposed an image registration method based on image partition with topography assistance. First, an elevation threshold is generated based on the UAV trajectories, and the measurement area is partitioned using the assisted topography. Then, a polynomial fitting model is constructed for offsets within each partition with constraints applied at the partition boundaries for joint optimization. Finally, continuous global offset fitting surfaces are obtained, and precise image registration is achieved by resampling the slave image. The effectiveness of the proposed method is preliminarily validated using real measurement data obtained from UAV InSAR in the P-band.
Miniaturized and lightweight Unmanned Aerial Vehicles (UAV) provide a flexible platform for Synthetic Aperture Radar (SAR). The application of UAV Interferometric SAR (InSAR) is gradually increasing in interferometric measurement fields. UAVs are small and light, which are easily affected by airflow disturbances. Their trajectories are nonlinear and unparallel when adopting the multipass mode for interferometry. The nonlinear and unparallel trajectories result in geometric distortion between the interferometric image pairs. Under complex topography conditions, the interferometric image pairs of UAVs have large offsets that are obviously space-dependent, thereby resulting in substantial technical challenges during image registration. Conventional image registration methods based on polynomial fitting are no longer applicable. In this study, we proposed an image registration method based on image partition with topography assistance. First, an elevation threshold is generated based on the UAV trajectories, and the measurement area is partitioned using the assisted topography. Then, a polynomial fitting model is constructed for offsets within each partition with constraints applied at the partition boundaries for joint optimization. Finally, continuous global offset fitting surfaces are obtained, and precise image registration is achieved by resampling the slave image. The effectiveness of the proposed method is preliminarily validated using real measurement data obtained from UAV InSAR in the P-band.
2024, 13(1): 134-149.
This paper proposes a radar signal transceiver framework that combines single-bit sampling and time division multiplexing receivers to satisfy the application requirements of low-cost lightweight radars. Firstly, this paper explains the advantages of saving the number of receivers by introducing the working principle of the framework. From the perspective of radar resource allocation, the importance of single-bit sampling in this framework was analyzed; additionally, the proposed framework can achieve better performance than a classical linear frequency modulation continuous wave radar using time and space exchange. Subsequently, the formulas for range, velocity and angle measurement were derived, along with the Cramér-Rao bound for estimating target parameters. Accordingly, the performance advantages of the proposed framework were verified, and the signal-to-noise ratio conditions for its stable operation were determined. Finally, this paper verifies the accuracy of the target acquisition principle of the proposed framework and the reliability of the performance analysis by using a velocity dimensional pairing algorithm based on single-bit two-dimensional multiple signal classification.
This paper proposes a radar signal transceiver framework that combines single-bit sampling and time division multiplexing receivers to satisfy the application requirements of low-cost lightweight radars. Firstly, this paper explains the advantages of saving the number of receivers by introducing the working principle of the framework. From the perspective of radar resource allocation, the importance of single-bit sampling in this framework was analyzed; additionally, the proposed framework can achieve better performance than a classical linear frequency modulation continuous wave radar using time and space exchange. Subsequently, the formulas for range, velocity and angle measurement were derived, along with the Cramér-Rao bound for estimating target parameters. Accordingly, the performance advantages of the proposed framework were verified, and the signal-to-noise ratio conditions for its stable operation were determined. Finally, this paper verifies the accuracy of the target acquisition principle of the proposed framework and the reliability of the performance analysis by using a velocity dimensional pairing algorithm based on single-bit two-dimensional multiple signal classification.
2024, 13(1): 150-159.
The traditional Direction Of Arrival (DOA) estimation is typically based on phased array antenna systems. However, it is greatly limited by the high hardware cost for applications in various fields. In addition, conventional phased array antennas also suffer from the high Radar Cross-Section (RCS), which cannot be employed for stealth purposes. To address these issues, we propose a new algorithm based on the Space-Time-coding (STC) strategy for simultaneous DOA estimation and RCS reduction, which is further experimentally verified using a metasurface in the millimeter band. The results demonstrate the excellent performance of the proposed DOA method with an error below 1°. Meanwhile, a good RCS reduction of over 10 dB is achieved in the bandwidth of interest. The proposed algorithm paves a new path to integrating DOA estimation and RCS reduction with a single metasurface, with the advantages of low cost and good performance.
The traditional Direction Of Arrival (DOA) estimation is typically based on phased array antenna systems. However, it is greatly limited by the high hardware cost for applications in various fields. In addition, conventional phased array antennas also suffer from the high Radar Cross-Section (RCS), which cannot be employed for stealth purposes. To address these issues, we propose a new algorithm based on the Space-Time-coding (STC) strategy for simultaneous DOA estimation and RCS reduction, which is further experimentally verified using a metasurface in the millimeter band. The results demonstrate the excellent performance of the proposed DOA method with an error below 1°. Meanwhile, a good RCS reduction of over 10 dB is achieved in the bandwidth of interest. The proposed algorithm paves a new path to integrating DOA estimation and RCS reduction with a single metasurface, with the advantages of low cost and good performance.
2024, 13(1): 160-173.
Orthogonal Frequency Division Multiplexing (OFDM) waveform design is one of the key physical layer technologies for achieving joint radar-communication. OFDM waveforms usually have issues with high Peak to Average Power Ratio (PAPR) and high waveform autocorrelation sidelobe levels. This paper proposes an integrated waveform design method based on data distortion to address the communication rate degradation problem of existing joint PAPR and autocorrelation sidelobe reduction methods. The paper also takes the Error Vector Magnitude (EVM) of communication data as one of the optimization objectives, reducing the communication bit error rate caused by data distortion. Firstly, an optimization model was constructed to minimize the Integrated Sidelobe Level Ratio (ISLR) and EVM under PAPR constraints. Secondly, based on the characteristics of the modulation constellation, the multi-objective high-dimensional non-convex optimization problem is transformed into two single objective optimization subproblems by using the data distortion of outer constellation modulation and all modulation data distortion. Convex relaxation operation and Alternating Direction Method of Multipliers (ADMM) are respectively used to solve the simplified subproblems, resulting in low ISLR waveform and low EVM waveform under PAPR constraint. The simulation results show that the integrated waveform designed by the proposed method can meet the requirements of PAPR, and has good sensing and communication performance.
Orthogonal Frequency Division Multiplexing (OFDM) waveform design is one of the key physical layer technologies for achieving joint radar-communication. OFDM waveforms usually have issues with high Peak to Average Power Ratio (PAPR) and high waveform autocorrelation sidelobe levels. This paper proposes an integrated waveform design method based on data distortion to address the communication rate degradation problem of existing joint PAPR and autocorrelation sidelobe reduction methods. The paper also takes the Error Vector Magnitude (EVM) of communication data as one of the optimization objectives, reducing the communication bit error rate caused by data distortion. Firstly, an optimization model was constructed to minimize the Integrated Sidelobe Level Ratio (ISLR) and EVM under PAPR constraints. Secondly, based on the characteristics of the modulation constellation, the multi-objective high-dimensional non-convex optimization problem is transformed into two single objective optimization subproblems by using the data distortion of outer constellation modulation and all modulation data distortion. Convex relaxation operation and Alternating Direction Method of Multipliers (ADMM) are respectively used to solve the simplified subproblems, resulting in low ISLR waveform and low EVM waveform under PAPR constraint. The simulation results show that the integrated waveform designed by the proposed method can meet the requirements of PAPR, and has good sensing and communication performance.
2024, 13(1): 174-186.
Due to the mismatch between transmit waveforms and receive filters, Cross-Ambiguity Function (CAF) shaping plays an important role in the design of cognitive radar waveforms and allows more freedom for waveform optimization problem than conventional ambiguity function shaping. A CAF shaping method is proposed for designing phase-shift keying transmit waveforms and receive filters jointly to maximize the output Signal-to-Interference-plus-Noise Ratio (SINR), thereby solving the problem of weaking-moving target detection under strong clutter conditions. The optimization problem is first modeled as a quadratic fractional programming problem under the Constant Modulus (CM) constraint of the transmit waveform. The conjugated gradient method is utilized to solve the minimization problem of the Stiefel manifold space through the introduction of auxiliary variables; furthermore the nonconvex optimization problem is converted into a unimodular quadratic programming problem. An algorithm based on alternately iterative maximization and power method-like iteration is proposed to solve the quadratic optimization problem. Since transmit waveforms are limited by hardware and achieving CM is difficult, the nearest vector method is employed under the constraint of a low peak-to-average power ratio. Finally, the experiments with simulated and real measured data under different parameters reveal that the transmit waveforms and receive filters designed using the proposed method exhibit better SINR performance and faster convergence speed compared with other existing algorithms.
Due to the mismatch between transmit waveforms and receive filters, Cross-Ambiguity Function (CAF) shaping plays an important role in the design of cognitive radar waveforms and allows more freedom for waveform optimization problem than conventional ambiguity function shaping. A CAF shaping method is proposed for designing phase-shift keying transmit waveforms and receive filters jointly to maximize the output Signal-to-Interference-plus-Noise Ratio (SINR), thereby solving the problem of weaking-moving target detection under strong clutter conditions. The optimization problem is first modeled as a quadratic fractional programming problem under the Constant Modulus (CM) constraint of the transmit waveform. The conjugated gradient method is utilized to solve the minimization problem of the Stiefel manifold space through the introduction of auxiliary variables; furthermore the nonconvex optimization problem is converted into a unimodular quadratic programming problem. An algorithm based on alternately iterative maximization and power method-like iteration is proposed to solve the quadratic optimization problem. Since transmit waveforms are limited by hardware and achieving CM is difficult, the nearest vector method is employed under the constraint of a low peak-to-average power ratio. Finally, the experiments with simulated and real measured data under different parameters reveal that the transmit waveforms and receive filters designed using the proposed method exhibit better SINR performance and faster convergence speed compared with other existing algorithms.
2024, 13(1): 187-199.
Dense false target jamming generates a large number of false targets around the real target, leading to dual jamming effects of deception and suppression. This severely affects the target detection ability of the radar. Therefore, this study proposes a range-Doppler two-dimensional jamming reconstruction algorithm based on the interpulse code agile waveform to suppress dense false target jamming. Based on the range-gating characteristics of the interpulse code agile waveform, the jamming and target echo reconstruction in the range-Doppler domain is realized by alternate inversion. Reconstruction jamming is eliminated by the iterative cancellation method. First, the jamming and target echo are processed by constructing receiving filter banks with different range intervals. Second, a joint mismatched filter bank is used to make the range sidelobe structure of each pulse filter output approximately the same. This reduces the divergence energy along the Doppler dimension after the pulse Doppler processing of the interpulse code agile waveform. The filter matrix is then constructed using the energy distribution characteristics of the jamming and target echo in different range-Doppler regions. Finally, accurate jamming and target echo reconstruction are achieved by alternate inversion to suppress dense false target jamming. Simulation results demonstrate the superior performance of the proposed algorithm in terms of jamming suppression and running time compared with traditional algorithms. These procedures significantly improve the target detection capability of the radar in strong jamming scenarios.
Dense false target jamming generates a large number of false targets around the real target, leading to dual jamming effects of deception and suppression. This severely affects the target detection ability of the radar. Therefore, this study proposes a range-Doppler two-dimensional jamming reconstruction algorithm based on the interpulse code agile waveform to suppress dense false target jamming. Based on the range-gating characteristics of the interpulse code agile waveform, the jamming and target echo reconstruction in the range-Doppler domain is realized by alternate inversion. Reconstruction jamming is eliminated by the iterative cancellation method. First, the jamming and target echo are processed by constructing receiving filter banks with different range intervals. Second, a joint mismatched filter bank is used to make the range sidelobe structure of each pulse filter output approximately the same. This reduces the divergence energy along the Doppler dimension after the pulse Doppler processing of the interpulse code agile waveform. The filter matrix is then constructed using the energy distribution characteristics of the jamming and target echo in different range-Doppler regions. Finally, accurate jamming and target echo reconstruction are achieved by alternate inversion to suppress dense false target jamming. Simulation results demonstrate the superior performance of the proposed algorithm in terms of jamming suppression and running time compared with traditional algorithms. These procedures significantly improve the target detection capability of the radar in strong jamming scenarios.
2024, 13(1): 200-214.
Random Stepped Frequency (RSF) radars can achieve high-range resolution with relatively low hardware complexity by synthesizing a wide bandwidth. Moreover, because of the random frequency agility of each pulse, the radars possess robust anti-interference and electromagnetic compatibility capabilities, rendering them invaluable for high-precision detection in complex electromagnetic environments. However, the inherent sparsity sensing of the radar waveform in the time-frequency domain, causes a lack of echo coherence information, leading to an underdetermined estimation of the traditional matched filter, which results in fluctuating high side lobes in the estimation spectrum and adversely deteriorating detection performance. This paper proposes a sparse recovery method based on Hankel matrix completion for the high-resolution range-Doppler spectrum of the RSF radars. Using the low-rank matrix completion concept, this method fills in the missing samples caused by sparse sensing for RSF radars, thereby restoring continuous coherence information and effectively addressing the underdetermined estimation issue. First, an undersampled data matrix of a single coarse-resolution range for RSF radar is constructed. Subsequently, the time-frequency data matrix is reconstructed into a double Hankel form, and its low-rank prior characteristics are analyzed and proven. Finally, the Alternating Direction Method of Multipliers (ADMM) algorithm is applied to restore the unsampled time-frequency data, ensuring sparse recovery of the high-resolution range-Doppler spectrum with low sidelobes. Simulations and real tests demonstrate the effectiveness and superiority of the proposed method.
Random Stepped Frequency (RSF) radars can achieve high-range resolution with relatively low hardware complexity by synthesizing a wide bandwidth. Moreover, because of the random frequency agility of each pulse, the radars possess robust anti-interference and electromagnetic compatibility capabilities, rendering them invaluable for high-precision detection in complex electromagnetic environments. However, the inherent sparsity sensing of the radar waveform in the time-frequency domain, causes a lack of echo coherence information, leading to an underdetermined estimation of the traditional matched filter, which results in fluctuating high side lobes in the estimation spectrum and adversely deteriorating detection performance. This paper proposes a sparse recovery method based on Hankel matrix completion for the high-resolution range-Doppler spectrum of the RSF radars. Using the low-rank matrix completion concept, this method fills in the missing samples caused by sparse sensing for RSF radars, thereby restoring continuous coherence information and effectively addressing the underdetermined estimation issue. First, an undersampled data matrix of a single coarse-resolution range for RSF radar is constructed. Subsequently, the time-frequency data matrix is reconstructed into a double Hankel form, and its low-rank prior characteristics are analyzed and proven. Finally, the Alternating Direction Method of Multipliers (ADMM) algorithm is applied to restore the unsampled time-frequency data, ensuring sparse recovery of the high-resolution range-Doppler spectrum with low sidelobes. Simulations and real tests demonstrate the effectiveness and superiority of the proposed method.
2024, 13(1): 215-226.
Time Domain Coding Metasurface (TDCM) is an emerging technology enabling dynamic modulation of electromagnetic waves. In response to the control characteristics of this technology, this paper presents a radar jamming method based on TDCM intrapulse and interpulse coding optimization. First, optimization models are established in fast and slow time domains. By optimizing intrapulse and interpulse phase coding, the energy redistribution of targets is achieved, thereby generating deceptive interference on the range-Doppler two-dimensional plot. Subsequently, a genetic algorithm is employed to solve this discrete optimization problem. Furthermore, this paper analyzes the effect of various modulation factors on interference effectiveness in terms of TDCM coding strategies, providing guidance for achieving optimal strategies for deceptive interference.
Time Domain Coding Metasurface (TDCM) is an emerging technology enabling dynamic modulation of electromagnetic waves. In response to the control characteristics of this technology, this paper presents a radar jamming method based on TDCM intrapulse and interpulse coding optimization. First, optimization models are established in fast and slow time domains. By optimizing intrapulse and interpulse phase coding, the energy redistribution of targets is achieved, thereby generating deceptive interference on the range-Doppler two-dimensional plot. Subsequently, a genetic algorithm is employed to solve this discrete optimization problem. Furthermore, this paper analyzes the effect of various modulation factors on interference effectiveness in terms of TDCM coding strategies, providing guidance for achieving optimal strategies for deceptive interference.
2024, 13(1): 227-239.
The aiming jamming emitted by self-defense jammers renders various passive anti-jamming measures based on signal processing ineffective, posing severe threats to modern radars. Frequency agility, as an active countermeasure, enables the resistance of aiming jamming. In response to issues such as the unstable anti-jamming performance of traditional random frequency hopping, limited freedom in frequency selection, and the long time required for strategic learning, the paper proposes a fast-adaptive frequency-hopping strategy for a frequency agile radar. First, a frequency agile waveform with repeatable frequency selection is designed, providing more choices for an optimal solution. Accordingly, using the data collected through continuous confrontation between a radar and a jammer, and the exploration and feedback mechanism of deep reinforcement learning, a frequency-selection strategy is continuously optimized. Specifically, considering radar frequency from the previous time and jamming frequency perceived at the current time as reinforcement learning inputs, the neural network intelligently selects each subpulse frequency at the current time and optimizes the strategy until it is optimal based on the anti-jamming effectiveness evaluated by the target detection result and Signal-to-Jamming-plus-Noise Ratio (SJNR). To improve the convergence speed of the optimal strategy, the designed input state is independent of the historical time step, the introduced greedy strategy balances the search-utilization mechanism, and the SJNR differentiates rewards more. Multiple sets of simulations show that the proposed method can converge to the optimal strategy and has high convergence efficiency.
The aiming jamming emitted by self-defense jammers renders various passive anti-jamming measures based on signal processing ineffective, posing severe threats to modern radars. Frequency agility, as an active countermeasure, enables the resistance of aiming jamming. In response to issues such as the unstable anti-jamming performance of traditional random frequency hopping, limited freedom in frequency selection, and the long time required for strategic learning, the paper proposes a fast-adaptive frequency-hopping strategy for a frequency agile radar. First, a frequency agile waveform with repeatable frequency selection is designed, providing more choices for an optimal solution. Accordingly, using the data collected through continuous confrontation between a radar and a jammer, and the exploration and feedback mechanism of deep reinforcement learning, a frequency-selection strategy is continuously optimized. Specifically, considering radar frequency from the previous time and jamming frequency perceived at the current time as reinforcement learning inputs, the neural network intelligently selects each subpulse frequency at the current time and optimizes the strategy until it is optimal based on the anti-jamming effectiveness evaluated by the target detection result and Signal-to-Jamming-plus-Noise Ratio (SJNR). To improve the convergence speed of the optimal strategy, the designed input state is independent of the historical time step, the introduced greedy strategy balances the search-utilization mechanism, and the SJNR differentiates rewards more. Multiple sets of simulations show that the proposed method can converge to the optimal strategy and has high convergence efficiency.
2024, 13(1): 240-252.
Interrupted Sampling Repeater Jamming (ISRJ) falls within the category of intrapulse coherent deception interference. ISRJ employs the principle of undersampling to engender multiple spurious target peaks on the range profile, thereby disrupting the detection and tracking of genuine targets. To address this challenge, this study introduces a novel method grounded in the waveform domain to mitigate ISRJ before matched filtering. First, considering the partial matching attributes of ISRJ, an expanded domain, specifically the waveform domain, is incorporated into the matched filtering. This augmentation enables the investigation of local features within the interference signals and components of authentic target echo signals. Moreover, adaptive threshold functions are defined for each waveform domain. Subsequently, the introduction of the Kalman filter enables the state estimation of waveform domain signals. Additionally, valid and invalid integral elements are discriminated within the waveform domain signals via adaptive threshold detection, and a state space estimation is formulated, specifically concerning the valid integral elements. In conclusion, by suppressing the invalid integral elements within the waveform domain signals, the proposed approach simultaneously supplements the estimated state space of valid integral elements with their corresponding length components. This preservation of residual valid integral elements, coupled with integration operation, yields a range profile outcome devoid of deceptive interference artifacts. Importantly, the approach proposed herein operates independently of any prior information regarding the interference device parameters, thereby substantially reducing the effect of ISRJ. Simulation experiments illustrate that, in comparison with traditional methodologies, the method proposed in this study exhibits remarkably superior resistance against the ISRJ interference challenges.
Interrupted Sampling Repeater Jamming (ISRJ) falls within the category of intrapulse coherent deception interference. ISRJ employs the principle of undersampling to engender multiple spurious target peaks on the range profile, thereby disrupting the detection and tracking of genuine targets. To address this challenge, this study introduces a novel method grounded in the waveform domain to mitigate ISRJ before matched filtering. First, considering the partial matching attributes of ISRJ, an expanded domain, specifically the waveform domain, is incorporated into the matched filtering. This augmentation enables the investigation of local features within the interference signals and components of authentic target echo signals. Moreover, adaptive threshold functions are defined for each waveform domain. Subsequently, the introduction of the Kalman filter enables the state estimation of waveform domain signals. Additionally, valid and invalid integral elements are discriminated within the waveform domain signals via adaptive threshold detection, and a state space estimation is formulated, specifically concerning the valid integral elements. In conclusion, by suppressing the invalid integral elements within the waveform domain signals, the proposed approach simultaneously supplements the estimated state space of valid integral elements with their corresponding length components. This preservation of residual valid integral elements, coupled with integration operation, yields a range profile outcome devoid of deceptive interference artifacts. Importantly, the approach proposed herein operates independently of any prior information regarding the interference device parameters, thereby substantially reducing the effect of ISRJ. Simulation experiments illustrate that, in comparison with traditional methodologies, the method proposed in this study exhibits remarkably superior resistance against the ISRJ interference challenges.
2024, 13(1): 253-269.
Appropriate and effective resource scheduling is the key to achieving the best performance for a space-based radar. Considering the resource scheduling problem of multi-target tracking in a space-based radar system, we establish a cost function that considers target threat, tracking accuracy, and Low Probability of Interception (LPI). Considering target uncertainty and constraints of the space-based platform and long-term expected cost, we establish a resource scheduling model based on the Partially Observable Markov Decision Process (POMDP) with multiple constraints. To transform and decompose the resource scheduling problem of multi-target tracking with multiple constraints into multiple unconstrained sub-problems, we use the Lagrangian relaxation method. To deal with the curse of dimensionality caused by the continuous state space, continuous action space and continuous observation space, we use the online POMDP algorithm based on the Monte Carlo Tree Search (MCTS) and partially observable Monte Carlo planning with observation widening algorithm. Finally, a non-myopic and fast resource scheduling algorithm with comprehensive performance indices for multi-target tracking in a space-based radar system is proposed. Simulation results show that the proposed algorithm, when compared with the existing scheduling algorithms, allocates resources more appropriately and shows better performance.
Appropriate and effective resource scheduling is the key to achieving the best performance for a space-based radar. Considering the resource scheduling problem of multi-target tracking in a space-based radar system, we establish a cost function that considers target threat, tracking accuracy, and Low Probability of Interception (LPI). Considering target uncertainty and constraints of the space-based platform and long-term expected cost, we establish a resource scheduling model based on the Partially Observable Markov Decision Process (POMDP) with multiple constraints. To transform and decompose the resource scheduling problem of multi-target tracking with multiple constraints into multiple unconstrained sub-problems, we use the Lagrangian relaxation method. To deal with the curse of dimensionality caused by the continuous state space, continuous action space and continuous observation space, we use the online POMDP algorithm based on the Monte Carlo Tree Search (MCTS) and partially observable Monte Carlo planning with observation widening algorithm. Finally, a non-myopic and fast resource scheduling algorithm with comprehensive performance indices for multi-target tracking in a space-based radar system is proposed. Simulation results show that the proposed algorithm, when compared with the existing scheduling algorithms, allocates resources more appropriately and shows better performance.
2024, 13(1): 270-282.
In many military and civilian areas, there exists a scenario in which multiple intruders from an adversary attempt to enter important region of our own to carry out intentional malign activity. Adversarial Risk (AR) estimation is used to assess and predict the expected damage to our valuable assets from the actions of online adversaries based on measurements performed by sensors. To evaluate random and time-varying AR, this study proposes a stochastic AR estimation approach based on a Labeled Multi-Bernoulli (LMB) tracker. First, in the formulation of LMB filtering, expressions of the minimum mean squared error estimation of the stochastic AR are derived for the additive and multiplying model. Second, by combining the Gaussian mixture and sampling approximations, we devise a numerical calculation approach for the proposed AR estimations. Third, we achieve an online evaluation of the expected damage to our valuable assets from the adversary by embedding the proposed AR estimation and LMB filtering. The effectiveness and performance advantage of the proposed estimation algorithms are verified using measurements from radars, considering a simulated scenario wherein multiple lethal targets hit the radar positions.
In many military and civilian areas, there exists a scenario in which multiple intruders from an adversary attempt to enter important region of our own to carry out intentional malign activity. Adversarial Risk (AR) estimation is used to assess and predict the expected damage to our valuable assets from the actions of online adversaries based on measurements performed by sensors. To evaluate random and time-varying AR, this study proposes a stochastic AR estimation approach based on a Labeled Multi-Bernoulli (LMB) tracker. First, in the formulation of LMB filtering, expressions of the minimum mean squared error estimation of the stochastic AR are derived for the additive and multiplying model. Second, by combining the Gaussian mixture and sampling approximations, we devise a numerical calculation approach for the proposed AR estimations. Third, we achieve an online evaluation of the expected damage to our valuable assets from the adversary by embedding the proposed AR estimation and LMB filtering. The effectiveness and performance advantage of the proposed estimation algorithms are verified using measurements from radars, considering a simulated scenario wherein multiple lethal targets hit the radar positions.
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