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1
2026, 15(4): 1125-1151.
Abstract
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Radar systems acquire target information by transmitting waveforms, receiving echoes, and processing signals; thus, waveform performance is a critical determinant of radar system performance. Compared with other radar systems, Synthetic Aperture Radar (SAR) operates under unique conditions, including distributed target scenes, waveforms with large time-bandwidth products, wide-swath and long-range imaging, and range-azimuth coupling. These characteristics impose additional stringent requirements on SAR waveform design. Drawing on the authors’ research and expertise in SAR waveform coding, this paper reviews recent domestic and international advances in SAR waveform design, discusses key technical challenges, and highlights the role of waveform design in enhancing system imaging performance. Finally, this study outlines future trends and potential directions for SAR waveform design methodologies.
Radar systems acquire target information by transmitting waveforms, receiving echoes, and processing signals; thus, waveform performance is a critical determinant of radar system performance. Compared with other radar systems, Synthetic Aperture Radar (SAR) operates under unique conditions, including distributed target scenes, waveforms with large time-bandwidth products, wide-swath and long-range imaging, and range-azimuth coupling. These characteristics impose additional stringent requirements on SAR waveform design. Drawing on the authors’ research and expertise in SAR waveform coding, this paper reviews recent domestic and international advances in SAR waveform design, discusses key technical challenges, and highlights the role of waveform design in enhancing system imaging performance. Finally, this study outlines future trends and potential directions for SAR waveform design methodologies.
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2026, 15(4): 1258-1277.
The threat from low-altitude targets to airspace security such as airport is increasing, making accurate detection and recognition essential for radar systems. High-quality measured radar datasets are crucial for advancing low-altitude target recognition. However, most existing public radar datasets for these targets consist of simulation data or short-range collected data, which have difficulty accurately reflecting and verifying radar target recognition performance in long-range scenarios. To overcome these limitations, this study creates a low-altitude target detection and recognition dataset based on Holographic Staring Radar (HSR), including measured data collection and recognition validation for typical low-altitude targets in outdoor environments. The dataset includes common targets such as multirotor unmanned aerial vehicles, sparrows, and large migratory birds, along with representative motion scenarios like hovering, circling, and radial flight. It also offers synchronized target micro-Doppler waterfall plots and radar-measured track information (including azimuth and elevation angles, radial velocity, and normalized signal-to-noise ratio), providing a data foundation for exploring the intrinsic link between target detailed features and motion states. Building on this, a multimodal adaptive feature fusion network is developed to extract and combine Doppler and kinematic features from different targets, demonstrating the dataset’s effectiveness in distinguishing various low-altitude targets.
The threat from low-altitude targets to airspace security such as airport is increasing, making accurate detection and recognition essential for radar systems. High-quality measured radar datasets are crucial for advancing low-altitude target recognition. However, most existing public radar datasets for these targets consist of simulation data or short-range collected data, which have difficulty accurately reflecting and verifying radar target recognition performance in long-range scenarios. To overcome these limitations, this study creates a low-altitude target detection and recognition dataset based on Holographic Staring Radar (HSR), including measured data collection and recognition validation for typical low-altitude targets in outdoor environments. The dataset includes common targets such as multirotor unmanned aerial vehicles, sparrows, and large migratory birds, along with representative motion scenarios like hovering, circling, and radial flight. It also offers synchronized target micro-Doppler waterfall plots and radar-measured track information (including azimuth and elevation angles, radial velocity, and normalized signal-to-noise ratio), providing a data foundation for exploring the intrinsic link between target detailed features and motion states. Building on this, a multimodal adaptive feature fusion network is developed to extract and combine Doppler and kinematic features from different targets, demonstrating the dataset’s effectiveness in distinguishing various low-altitude targets.
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2026, 15(4): 1340-1359.
Reinforcement Learning (RL) is a critical approach for enabling cognitive radar target detection. Existing studies primarily focus on detection methods for centralized Multiple-Input Multiple-Output (MIMO) radar, which are limited to a single observation perspective. To address this issue, this paper proposes an RL-based multi-target detection method for a distributed MIMO radar system that possesses waveform and spatial diversity. The proposed method exploits spatial diversity to ensure robust target detection, while waveform diversity is used to construct a Markov decision process. Specifically, the radar first perceives target attributes through statistical signal detection techniques, then optimizes the transmit waveform accordingly, and iteratively updates its understanding of the environmental context using accumulated experience. This cyclic process gradually converges, yielding radar waveforms focused on target directions and achieving improved detection performance. To facilitate target localization, a maximization grid-based generalized likelihood ratio test detector for multi-antenna configurations is derived, using regularly shaped grids as the cell under test. For waveform optimization, two types of optimization problems, namely conventional and strong-target-limited formulations, are developed, and their solutions are obtained using continuous convex approximation. Simulation results across static and dynamic scenarios demonstrate that the proposed method can autonomously perceive environmental context and achieve superior detection performance compared with benchmark methods, particularly in weak target detection.
Reinforcement Learning (RL) is a critical approach for enabling cognitive radar target detection. Existing studies primarily focus on detection methods for centralized Multiple-Input Multiple-Output (MIMO) radar, which are limited to a single observation perspective. To address this issue, this paper proposes an RL-based multi-target detection method for a distributed MIMO radar system that possesses waveform and spatial diversity. The proposed method exploits spatial diversity to ensure robust target detection, while waveform diversity is used to construct a Markov decision process. Specifically, the radar first perceives target attributes through statistical signal detection techniques, then optimizes the transmit waveform accordingly, and iteratively updates its understanding of the environmental context using accumulated experience. This cyclic process gradually converges, yielding radar waveforms focused on target directions and achieving improved detection performance. To facilitate target localization, a maximization grid-based generalized likelihood ratio test detector for multi-antenna configurations is derived, using regularly shaped grids as the cell under test. For waveform optimization, two types of optimization problems, namely conventional and strong-target-limited formulations, are developed, and their solutions are obtained using continuous convex approximation. Simulation results across static and dynamic scenarios demonstrate that the proposed method can autonomously perceive environmental context and achieve superior detection performance compared with benchmark methods, particularly in weak target detection.
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2026, 15(4): 1238-1257.
To address issues such as insufficient feature extraction, limited spatiotemporal correlation modeling, and poor classification performance in radar classification of sea-air targets, this paper investigates on graph network-based feature extraction and classification methods. First, focusing on digital array ubiquitous radar, a radar detection dataset for sea-air targets, named LSS-DAUR-1.0, is constructed; it contains Doppler and track data for six types of targets: Passenger ships, speedboats, helicopters, rotor drones, birds, and fixed-wing drones. Second, based on this dataset, the multidomain and multidimensional characteristics of the targets are analyzed, and the complementarity between Doppler and physical motion features is verified through correlation and cosine similarity analyses. On this basis, a Graph Convolutional Network with Dynamic Graph Construction (DG-GCN) classification method fusing dual features is proposed. An adaptive window adjustment, a hybrid attenuation function, and a dynamic threshold mechanism are designed to construct an adaptive dynamic graph based on spatiotemporal correlation. Combined with graph convolution-based feature learning and classification modules, this approach achieves refined classification of low, slow, and small targets. Validation on the LSS-DAUR-1.0 dataset shows that the DG-GCN achieves 99.66% classification accuracy, which is 6.78% and 17.97% higher than that of ResNet and Transformer models, respectively. The total processing time is only 4.98 ms, which is more than 80% lower than that of the aforementioned comparison models. Hence, the DG-GCN achieves both high accuracy and efficiency. In addition, noise environment tests show good robustness. Ablation experiments verify that the dynamic edge weight mechanism compensates for the lack of spatial feature correlation in purely temporal connections and improves the model’s generalizability.
To address issues such as insufficient feature extraction, limited spatiotemporal correlation modeling, and poor classification performance in radar classification of sea-air targets, this paper investigates on graph network-based feature extraction and classification methods. First, focusing on digital array ubiquitous radar, a radar detection dataset for sea-air targets, named LSS-DAUR-1.0, is constructed; it contains Doppler and track data for six types of targets: Passenger ships, speedboats, helicopters, rotor drones, birds, and fixed-wing drones. Second, based on this dataset, the multidomain and multidimensional characteristics of the targets are analyzed, and the complementarity between Doppler and physical motion features is verified through correlation and cosine similarity analyses. On this basis, a Graph Convolutional Network with Dynamic Graph Construction (DG-GCN) classification method fusing dual features is proposed. An adaptive window adjustment, a hybrid attenuation function, and a dynamic threshold mechanism are designed to construct an adaptive dynamic graph based on spatiotemporal correlation. Combined with graph convolution-based feature learning and classification modules, this approach achieves refined classification of low, slow, and small targets. Validation on the LSS-DAUR-1.0 dataset shows that the DG-GCN achieves 99.66% classification accuracy, which is 6.78% and 17.97% higher than that of ResNet and Transformer models, respectively. The total processing time is only 4.98 ms, which is more than 80% lower than that of the aforementioned comparison models. Hence, the DG-GCN achieves both high accuracy and efficiency. In addition, noise environment tests show good robustness. Ablation experiments verify that the dynamic edge weight mechanism compensates for the lack of spatial feature correlation in purely temporal connections and improves the model’s generalizability.
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2025, 14(5): 1276-1293.
This study addresses the issue of fine-grained feature extraction and classification for Low-Slow-Small (LSS) targets, such as birds and drones, by proposing a multi-band multi-angle feature fusion classification method. First, data from five types of rotorcraft drones and bird models were collected at multiple angles using K-band and L-band frequency-modulated continuous-wave radars, forming a dataset for LSS target detection. Second, to capture the periodic vibration characteristics of the L-band target signals, empirical mode decomposition was applied to extract high-frequency features and reduce noise interference. For the K-band echo signals, short-time Fourier transform was applied to obtain high-resolution micro-Doppler features from various angles. Based on these features, a Multi-band Multi-angle Feature Fusion Network (MMFFNet) was designed, incorporating an improved convolutional long short-term memory network for temporal feature extraction, along with an attention fusion module and a multiscale feature fusion module. The proposed architecture improves target classification accuracy by integrating features from both bands and angles. Validation using a real-world dataset showed that compared with methods relying on single radar features, the proposed approach improved the classification accuracy for seven types of LSS targets by 3.1% under a high Signal-to-Noise Ratio (SNR) of 5 dB and by 12.3% under a low SNR of −3 dB.
This study addresses the issue of fine-grained feature extraction and classification for Low-Slow-Small (LSS) targets, such as birds and drones, by proposing a multi-band multi-angle feature fusion classification method. First, data from five types of rotorcraft drones and bird models were collected at multiple angles using K-band and L-band frequency-modulated continuous-wave radars, forming a dataset for LSS target detection. Second, to capture the periodic vibration characteristics of the L-band target signals, empirical mode decomposition was applied to extract high-frequency features and reduce noise interference. For the K-band echo signals, short-time Fourier transform was applied to obtain high-resolution micro-Doppler features from various angles. Based on these features, a Multi-band Multi-angle Feature Fusion Network (MMFFNet) was designed, incorporating an improved convolutional long short-term memory network for temporal feature extraction, along with an attention fusion module and a multiscale feature fusion module. The proposed architecture improves target classification accuracy by integrating features from both bands and angles. Validation using a real-world dataset showed that compared with methods relying on single radar features, the proposed approach improved the classification accuracy for seven types of LSS targets by 3.1% under a high Signal-to-Noise Ratio (SNR) of 5 dB and by 12.3% under a low SNR of −3 dB.
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2026, 15(4): 1152-1167.
To address the critical issue of weak-target imaging performance in Synthetic Aperture Radar (SAR), this paper proposes a method for jointly designing the SAR waveform and imaging filter based on the Signal-to-Clutter-plus-Noise Ratio (SCNR) criterion. The method first establishes a joint optimization model aimed at maximizing the SCNR. This model comprehensively considers constant modulus constraints, mainlobe peak constraints, sidelobe levels, and target scattering uncertainty, resulting in a nonconvex, multiconstrained optimization problem with a max-min structure. To solve this problem, this paper develops a sequential alternating optimization framework that decomposes the original problem into two subproblems: waveform design and filter design. A strategy combining the epigraph transformation with the majorization-minimization algorithm is employed to handle the nonconvex fractional objective function and constraints. Auxiliary variables are introduced to extend the feasible region, thereby guaranteeing feasibility at the initial iteration. A penalty term that increases progressively with the number of iterations is then incorporated, ultimately forcing the auxiliary variables to approach zero and thereby enabling the solution of the original problem. The convergence and computational complexity of the proposed algorithm are also analyzed. Experimental results demonstrate that, compared with traditional linear frequency-modulated signals with identical parameters, the joint design scheme exhibits pronounced performance advantages in simulated, semi-physical, and field scenarios. The proposed method improves the SCNR of SAR images by more than 3.2 dB, providing an engineering-feasible approach for enhancing the detection and imaging performance of SAR systems for weak targets.
To address the critical issue of weak-target imaging performance in Synthetic Aperture Radar (SAR), this paper proposes a method for jointly designing the SAR waveform and imaging filter based on the Signal-to-Clutter-plus-Noise Ratio (SCNR) criterion. The method first establishes a joint optimization model aimed at maximizing the SCNR. This model comprehensively considers constant modulus constraints, mainlobe peak constraints, sidelobe levels, and target scattering uncertainty, resulting in a nonconvex, multiconstrained optimization problem with a max-min structure. To solve this problem, this paper develops a sequential alternating optimization framework that decomposes the original problem into two subproblems: waveform design and filter design. A strategy combining the epigraph transformation with the majorization-minimization algorithm is employed to handle the nonconvex fractional objective function and constraints. Auxiliary variables are introduced to extend the feasible region, thereby guaranteeing feasibility at the initial iteration. A penalty term that increases progressively with the number of iterations is then incorporated, ultimately forcing the auxiliary variables to approach zero and thereby enabling the solution of the original problem. The convergence and computational complexity of the proposed algorithm are also analyzed. Experimental results demonstrate that, compared with traditional linear frequency-modulated signals with identical parameters, the joint design scheme exhibits pronounced performance advantages in simulated, semi-physical, and field scenarios. The proposed method improves the SCNR of SAR images by more than 3.2 dB, providing an engineering-feasible approach for enhancing the detection and imaging performance of SAR systems for weak targets.
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2026, 15(4): 1185-1200.
To address the high imaging sidelobe levels associated with existing antijamming waveform design methods for Synthetic Aperture Radar (SAR) under Interrupted Sampling Repeater Jamming (ISRJ), this paper exploits the degrees of freedom provided by coherent multipulse integration in SAR imaging. Multipulse complementarity enables simultaneous improvement of antijamming capability and low-sidelobe imaging performance. Specifically, an SAR Point Spread Function (PSF) signal model is first established for the azimuth multipulse complementary waveform mode. Based on this model, a nonconvex multiobjective joint optimization framework is developed to maximize jamming suppression and minimize PSF sidelobe levels. To solve this challenging optimization problem, a two-Dimensional Joint Optimization of Complementary waveforms for Anti-ISRJ (2D-JOCA) algorithm is proposed. By adopting an alternating iterative optimization framework, the algorithm decomposes the original problem into two subproblems—waveform and filter optimization—and achieves their cooperative design through closed-form updates. Experimental results demonstrate that, compared with the best results of existing traditional single-waveform antijamming methods, the proposed approach improves the Jamming Integrated Level Ratio (JILR) and Jamming Peak Level Ratio (JPLR) by 12.85 and 3.83 dB, respectively, while reducing the Integrated Sidelobe Ratio (ISLR) and Peak Sidelobe Ratio (PSLR) by 24.96 and 11.40 dB, respectively. The proposed approach not only significantly enhances antijamming capability but also effectively suppresses range sidelobes and range-azimuth coupled sidelobes, thereby improving SAR imaging quality in complex electromagnetic interference environments.
To address the high imaging sidelobe levels associated with existing antijamming waveform design methods for Synthetic Aperture Radar (SAR) under Interrupted Sampling Repeater Jamming (ISRJ), this paper exploits the degrees of freedom provided by coherent multipulse integration in SAR imaging. Multipulse complementarity enables simultaneous improvement of antijamming capability and low-sidelobe imaging performance. Specifically, an SAR Point Spread Function (PSF) signal model is first established for the azimuth multipulse complementary waveform mode. Based on this model, a nonconvex multiobjective joint optimization framework is developed to maximize jamming suppression and minimize PSF sidelobe levels. To solve this challenging optimization problem, a two-Dimensional Joint Optimization of Complementary waveforms for Anti-ISRJ (2D-JOCA) algorithm is proposed. By adopting an alternating iterative optimization framework, the algorithm decomposes the original problem into two subproblems—waveform and filter optimization—and achieves their cooperative design through closed-form updates. Experimental results demonstrate that, compared with the best results of existing traditional single-waveform antijamming methods, the proposed approach improves the Jamming Integrated Level Ratio (JILR) and Jamming Peak Level Ratio (JPLR) by 12.85 and 3.83 dB, respectively, while reducing the Integrated Sidelobe Ratio (ISLR) and Peak Sidelobe Ratio (PSLR) by 24.96 and 11.40 dB, respectively. The proposed approach not only significantly enhances antijamming capability but also effectively suppresses range sidelobes and range-azimuth coupled sidelobes, thereby improving SAR imaging quality in complex electromagnetic interference environments.
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2023, 12(4): 906-922.
This study proposes a Synthetic Aperture Radar (SAR) aircraft detection and recognition method combined with scattering perception to address the problem of target discreteness and false alarms caused by strong background interference in SAR images. The global information is enhanced through a context-guided feature pyramid module, which suppresses strong disturbances in complex images and improves the accuracy of detection and recognition. Additionally, scatter key points are used to locate targets, and a scatter-aware detection module is designed to realize the fine correction of the regression boxes to improve target localization accuracy. This study generates and presents a high-resolution SAR-AIRcraft-1.0 dataset to verify the effectiveness of the proposed method and promote the research on SAR aircraft detection and recognition. The images in this dataset are obtained from the satellite Gaofen-3, which contains 4,368 images and 16,463 aircraft instances, covering seven aircraft categories, namely A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and other. We apply the proposed method and common deep learning algorithms to the constructed dataset. The experimental results demonstrate the excellent effectiveness of our method combined with scattering perception. Furthermore, we establish benchmarks for the performance indicators of the dataset in different tasks such as SAR aircraft detection, recognition, and integrated detection and recognition.
This study proposes a Synthetic Aperture Radar (SAR) aircraft detection and recognition method combined with scattering perception to address the problem of target discreteness and false alarms caused by strong background interference in SAR images. The global information is enhanced through a context-guided feature pyramid module, which suppresses strong disturbances in complex images and improves the accuracy of detection and recognition. Additionally, scatter key points are used to locate targets, and a scatter-aware detection module is designed to realize the fine correction of the regression boxes to improve target localization accuracy. This study generates and presents a high-resolution SAR-AIRcraft-1.0 dataset to verify the effectiveness of the proposed method and promote the research on SAR aircraft detection and recognition. The images in this dataset are obtained from the satellite Gaofen-3, which contains 4,368 images and 16,463 aircraft instances, covering seven aircraft categories, namely A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and other. We apply the proposed method and common deep learning algorithms to the constructed dataset. The experimental results demonstrate the excellent effectiveness of our method combined with scattering perception. Furthermore, we establish benchmarks for the performance indicators of the dataset in different tasks such as SAR aircraft detection, recognition, and integrated detection and recognition.
9
2023, 12(2): 456-469.
Marine target detection and recognition depend on the characteristics of marine targets and sea clutter. Therefore, understanding the essential features of marine targets based on the measured data is crucial for advancing target detection and recognition technology. To address the issue of insufficient data on the scattering characteristics of marine targets, the Sea-Detecting Radar Data-Sharing Program (SDRDSP) was upgraded to obtain data on marine targets and their environment under different polarizations and sea states. This upgrade expanded the physical dimension of radar target observation and improved radar and auxiliary data acquisition capabilities. Furthermore, a dual-polarized multistate scattering characteristic dataset of marine targets was constructed, and the statistical distribution characteristics, time and space correlation, and Doppler spectrum were analyzed, supporting the data usage. In the future, the types and quantities of maritime targets will continue to accumulate, providing data support for improving marine target detection and recognition performance and intelligence.
Marine target detection and recognition depend on the characteristics of marine targets and sea clutter. Therefore, understanding the essential features of marine targets based on the measured data is crucial for advancing target detection and recognition technology. To address the issue of insufficient data on the scattering characteristics of marine targets, the Sea-Detecting Radar Data-Sharing Program (SDRDSP) was upgraded to obtain data on marine targets and their environment under different polarizations and sea states. This upgrade expanded the physical dimension of radar target observation and improved radar and auxiliary data acquisition capabilities. Furthermore, a dual-polarized multistate scattering characteristic dataset of marine targets was constructed, and the statistical distribution characteristics, time and space correlation, and Doppler spectrum were analyzed, supporting the data usage. In the future, the types and quantities of maritime targets will continue to accumulate, providing data support for improving marine target detection and recognition performance and intelligence.
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2026, 15(4): 1168-1184.
High-Resolution Wide-Swath (HRWS) imaging is a key development direction for next-generation spaceborne Synthetic Aperture Radar (SAR) systems. Multiple-Input Multiple-Output (MIMO) SAR systems offer high spatial degrees of freedom, enabling enhanced system performance. However, effectively separating echoes from different transmit channels in MIMO-SAR systems is key to unlocking their advantages in spatial degrees of freedom. In this regard, a novel Space-Time Phase-Coded (STPC) waveform for MIMO-SAR systems is proposed based on the phase characteristics of SAR signals and the space-time properties of the “stop-and-go” model. This waveform modulates transmitted signals in the range dimension via phase coding and emits them at distinct spatial positions within each pulse repetition period, following a preset coding sequence. Upon reception, demodulating aliased echoes using receiver timing matched to the transmitter enables the efficient separation of echoes from different transmit channels. The proposed scheme can be integrated with existing classical azimuth multichannel reconstruction methods, effectively mitigating the trade-off between Pulse Repetition Frequency (PRF) and echo separability. Compared with the Alamouti, Short-Term Shift-Orthogonal (STSO), and Segmented Phase Code (SPC) waveforms in current MIMO-SAR systems, the STPC approach reduces antenna requirements by nearly 50%, thereby lowering the cost and complexity of hardware implementation. Simulation experiments on point targets and distributed scenes verify that the proposed waveform and processing scheme effectively suppress interwaveform interference and deliver strong imaging performance.
High-Resolution Wide-Swath (HRWS) imaging is a key development direction for next-generation spaceborne Synthetic Aperture Radar (SAR) systems. Multiple-Input Multiple-Output (MIMO) SAR systems offer high spatial degrees of freedom, enabling enhanced system performance. However, effectively separating echoes from different transmit channels in MIMO-SAR systems is key to unlocking their advantages in spatial degrees of freedom. In this regard, a novel Space-Time Phase-Coded (STPC) waveform for MIMO-SAR systems is proposed based on the phase characteristics of SAR signals and the space-time properties of the “stop-and-go” model. This waveform modulates transmitted signals in the range dimension via phase coding and emits them at distinct spatial positions within each pulse repetition period, following a preset coding sequence. Upon reception, demodulating aliased echoes using receiver timing matched to the transmitter enables the efficient separation of echoes from different transmit channels. The proposed scheme can be integrated with existing classical azimuth multichannel reconstruction methods, effectively mitigating the trade-off between Pulse Repetition Frequency (PRF) and echo separability. Compared with the Alamouti, Short-Term Shift-Orthogonal (STSO), and Segmented Phase Code (SPC) waveforms in current MIMO-SAR systems, the STPC approach reduces antenna requirements by nearly 50%, thereby lowering the cost and complexity of hardware implementation. Simulation experiments on point targets and distributed scenes verify that the proposed waveform and processing scheme effectively suppress interwaveform interference and deliver strong imaging performance.
11
2019, 8(5): 656-667.
To meet the radar data requirements of target detection technology research and address the lack of publicly available sea-detecting radar data, a data-sharing program for sea-detecting radar is proposed herein. The aim of the proposed data-sharing program is to conduct sea detection experiments using an X-band solidstate phase-coherent radar and other multi-type radars to obtain the target and sea clutter data under different sea conditions, resolutions, and grazing angles. Moreover, the marine meteorological and hydrological data, target position, and track data are simultaneously obtained using the proposed data-sharing program to help achieve the standardized management of radar-measured data. The proposed data-sharing program aims to promote the open sharing of data sets, serve as the basis for research on sea clutter characteristics, and facilitate the research on sea clutter suppression and target detection technology.
To meet the radar data requirements of target detection technology research and address the lack of publicly available sea-detecting radar data, a data-sharing program for sea-detecting radar is proposed herein. The aim of the proposed data-sharing program is to conduct sea detection experiments using an X-band solidstate phase-coherent radar and other multi-type radars to obtain the target and sea clutter data under different sea conditions, resolutions, and grazing angles. Moreover, the marine meteorological and hydrological data, target position, and track data are simultaneously obtained using the proposed data-sharing program to help achieve the standardized management of radar-measured data. The proposed data-sharing program aims to promote the open sharing of data sets, serve as the basis for research on sea clutter characteristics, and facilitate the research on sea clutter suppression and target detection technology.
12
2026, 15(4): 1306-1321.
In near-field imaging with Multiple-Input Multiple-Output (MIMO) radar, spatial resolution is effectively enhanced by extending the aperture of a two-dimensional MIMO array. The proposed system is based on a time-division multiple-access waveform and performs near-field aperture synthesis imaging using the MIMO array. High-resolution three-dimensional coverage of the near-field region is achieved by coherently accumulating multichannel raw echo data in the wavenumber domain. Compared with traditional mechanical scanning, this system is considered more suitable for scenarios with extremely high real-time requirements, such as civil aviation security inspection. However, millimeter waves have a short wavelength, so numerous transmit/receive elements must be placed in MIMO arrays to satisfy the Nyquist sampling criterion. This necessity leads to a substantial resource overhead. Thus, the Cooperative Multi-Constraint of Sparse Array (CMC-SA) algorithm is proposed for MIMO radar near-field imaging. Under the constraints of maintaining constant main lobe gain and suppressing sidelobe levels in the array pattern, an optimization model for near-field MIMO radar array configurations is constructed, with the weight \begin{document}$ {\ell}_{\rm P} $\end{document} \begin{document}$ {\ell}_{\rm P} $\end{document}
13
2026, 15(4): 1201-1219.
The automatic target recognition performance of radar is critically dependent on the quality of features extracted from target echo signals. As the information carrier that actively shapes echo signals, the transmitted waveform substantially affects the target classification performance. However, conventional waveform design is often decoupled from classifier optimization, thereby ignoring the critical synergy between the two. This disconnect, combined with the lack of a direct link between waveform optimization criteria and task-specific classification metrics, limits the target classification performance. Most existing approaches are confined to monostatic radar models. Further, they fail to establish relationships between the target’s aspect angle, the transmitted waveform, and classification performance, and lack a cooperative waveform design mechanism among nodes. Hence, they are unable to achieve spatial and waveform diversity gains. To overcome these limitations, this paper proposes an end-to-end “waveform aspect matching” optimization framework for target classification in distributed radar systems. This framework parameterizes the waveform as a trainable waveform generation module, cascaded with a downstream classification network. This transforms the isolated waveform design problem into a joint optimization of the waveform and classifier, directly guided by the classification task. Leveraging prior target information, the model is trained to jointly optimize and produce aspect-matched waveforms along with the corresponding classification network. Furthermore, to enhance the classification performance in distributed radar systems, a dual-branch network based on noncausal state-space duality modules is proposed to extract and fuse multiview information. Experimental results demonstrate that the proposed method can synergistically utilize waveform and spatial diversity to improve the target classification performance. It demonstrates robustness against node failures, offering a novel solution for intelligent waveform design in distributed radar systems.
The automatic target recognition performance of radar is critically dependent on the quality of features extracted from target echo signals. As the information carrier that actively shapes echo signals, the transmitted waveform substantially affects the target classification performance. However, conventional waveform design is often decoupled from classifier optimization, thereby ignoring the critical synergy between the two. This disconnect, combined with the lack of a direct link between waveform optimization criteria and task-specific classification metrics, limits the target classification performance. Most existing approaches are confined to monostatic radar models. Further, they fail to establish relationships between the target’s aspect angle, the transmitted waveform, and classification performance, and lack a cooperative waveform design mechanism among nodes. Hence, they are unable to achieve spatial and waveform diversity gains. To overcome these limitations, this paper proposes an end-to-end “waveform aspect matching” optimization framework for target classification in distributed radar systems. This framework parameterizes the waveform as a trainable waveform generation module, cascaded with a downstream classification network. This transforms the isolated waveform design problem into a joint optimization of the waveform and classifier, directly guided by the classification task. Leveraging prior target information, the model is trained to jointly optimize and produce aspect-matched waveforms along with the corresponding classification network. Furthermore, to enhance the classification performance in distributed radar systems, a dual-branch network based on noncausal state-space duality modules is proposed to extract and fuse multiview information. Experimental results demonstrate that the proposed method can synergistically utilize waveform and spatial diversity to improve the target classification performance. It demonstrates robustness against node failures, offering a novel solution for intelligent waveform design in distributed radar systems.
14
Fast Structured Sparse Millimeter-wave 3D SAR Imaging Based on Low-rank and Smooth Matrix Completion
2026, 15(4): 1417-1433.
The millimeter-Wave (mmWave) radar is widely used in security screening, nondestructive testing, and through-the-wall imaging due to its compact size, high resolution, and strong penetration capability. High-resolution mmWave radar imaging typically requires synthetic aperture emulation, which involves dense two-dimensional spatial sampling via structured scanning on a mechanical platform. However, this process is time-consuming in practical applications. Therefore, many existing studies have focused on reconstructing echo data under sparse sampling conditions for imaging. However, most existing sparse recovery methods assume uniformly random sparse sampling or involve high computational complexity, making them difficult to apply in practical Synthetic Aperture Radar (SAR) imaging systems. This paper proposes a fast, structured sparse, mmWave three-Dimensional (3D) SAR imaging algorithm based on low-rank and smooth Matrix Completion (MC) to address this problem. First, the global low-rank property and local smoothness prior of echo data are analyzed within the framework of near-field mmWave SAR imaging theory. Our analysis demonstrated that structured sparse SAR data arising from missing entire rows or columns in practical scanning can be recovered. Building on this, an MC model incorporating low-rank and smoothness constraints was constructed. This MC model jointly regularizes with nuclear norm and total variation and can be solved efficiently using the Alternating Direction Method of Multipliers (ADMM). Finally, the performance of the proposed algorithm was validated through multiple simulation runs and real-world experiments. Experimental results showed that, using only 20%–30% of randomly sampled rows or columns of echo data, the proposed algorithm can achieve fast data recovery and high-resolution 3D imaging within tens of seconds.
The millimeter-Wave (mmWave) radar is widely used in security screening, nondestructive testing, and through-the-wall imaging due to its compact size, high resolution, and strong penetration capability. High-resolution mmWave radar imaging typically requires synthetic aperture emulation, which involves dense two-dimensional spatial sampling via structured scanning on a mechanical platform. However, this process is time-consuming in practical applications. Therefore, many existing studies have focused on reconstructing echo data under sparse sampling conditions for imaging. However, most existing sparse recovery methods assume uniformly random sparse sampling or involve high computational complexity, making them difficult to apply in practical Synthetic Aperture Radar (SAR) imaging systems. This paper proposes a fast, structured sparse, mmWave three-Dimensional (3D) SAR imaging algorithm based on low-rank and smooth Matrix Completion (MC) to address this problem. First, the global low-rank property and local smoothness prior of echo data are analyzed within the framework of near-field mmWave SAR imaging theory. Our analysis demonstrated that structured sparse SAR data arising from missing entire rows or columns in practical scanning can be recovered. Building on this, an MC model incorporating low-rank and smoothness constraints was constructed. This MC model jointly regularizes with nuclear norm and total variation and can be solved efficiently using the Alternating Direction Method of Multipliers (ADMM). Finally, the performance of the proposed algorithm was validated through multiple simulation runs and real-world experiments. Experimental results showed that, using only 20%–30% of randomly sampled rows or columns of echo data, the proposed algorithm can achieve fast data recovery and high-resolution 3D imaging within tens of seconds.
15
2026, 15(4): 1322-1339.
This study addresses time-frequency synchronization errors in distributed Multiple-Input Multiple-Output (MIMO) radar systems and proposes a joint estimation method for target parameters and system time-frequency biases based on multitemporal measurement data. The method overcomes the limitations of traditional approaches that rely on singletemporal measurement data and direct-path signals, enabling high-accuracy joint parameter estimation through multiepoch data fusion without requiring direct-path information. The proposed method adopts a two-step strategy that combines a closed-form solution with iterative optimization. First, a closed-form solution is derived within a two-stage weighted least-squares framework using only the first- and last-epoch observations to obtain initial estimates of the target position, velocity, and auxiliary variables. This stage explicitly models second-order error terms and optimizes the construction of the weighting matrix, significantly improving accuracy and robustness under high-error conditions. Second, using the closed-form estimates as initialization, a maximum likelihood-maximum a posteriori objective function is formulated based on the full multiepoch measurement data, and a trust-region iterative optimization method is applied to refine the estimates and recover the time-frequency bias parameters. Simulation results show that the proposed method outperforms existing approaches across various error levels and geometric configurations, significantly enhancing the accuracy and robustness of target localization, velocity estimation, and time-frequency bias estimation. These results demonstrate strong theoretical significance and promising practical application potential.
This study addresses time-frequency synchronization errors in distributed Multiple-Input Multiple-Output (MIMO) radar systems and proposes a joint estimation method for target parameters and system time-frequency biases based on multitemporal measurement data. The method overcomes the limitations of traditional approaches that rely on singletemporal measurement data and direct-path signals, enabling high-accuracy joint parameter estimation through multiepoch data fusion without requiring direct-path information. The proposed method adopts a two-step strategy that combines a closed-form solution with iterative optimization. First, a closed-form solution is derived within a two-stage weighted least-squares framework using only the first- and last-epoch observations to obtain initial estimates of the target position, velocity, and auxiliary variables. This stage explicitly models second-order error terms and optimizes the construction of the weighting matrix, significantly improving accuracy and robustness under high-error conditions. Second, using the closed-form estimates as initialization, a maximum likelihood-maximum a posteriori objective function is formulated based on the full multiepoch measurement data, and a trust-region iterative optimization method is applied to refine the estimates and recover the time-frequency bias parameters. Simulation results show that the proposed method outperforms existing approaches across various error levels and geometric configurations, significantly enhancing the accuracy and robustness of target localization, velocity estimation, and time-frequency bias estimation. These results demonstrate strong theoretical significance and promising practical application potential.
16
2025, 14(1): 73-90.
Millimeter-wave radar is increasingly being adopted for smart home systems, elder care, and surveillance monitoring, owing to its adaptability to environmental conditions, high resolution, and privacy-preserving capabilities. A key factor in effectively utilizing millimeter-wave radar is the analysis of point clouds, which are essential for recognizing human postures. However, the sparse nature of these point clouds poses significant challenges for accurate and efficient human action recognition. To overcome these issues, we present a 3D point cloud dataset tailored for human actions captured using millimeter-wave radar (mmWave-3DPCHM-1.0). This dataset is enhanced with advanced data processing techniques and cutting-edge human action recognition models. Data collection is conducted using Texas Instruments (TI)’s IWR1443-ISK and Vayyar’s vBlu radio imaging module, covering 12 common human actions, including walking, waving, standing, and falling. At the core of our approach is the Point EdgeConv and Transformer (PETer) network, which integrates edge convolution with transformer models. For each 3D point cloud frame, PETer constructs a locally directed neighborhood graph through edge convolution to extract spatial geometric features effectively. The network then leverages a series of Transformer encoding models to uncover temporal relationships across multiple point cloud frames. Extensive experiments reveal that the PETer network achieves exceptional recognition rates of 98.77% on the TI dataset and 99.51% on the Vayyar dataset, outperforming the traditional optimal baseline model by approximately 5%. With a compact model size of only 1.09 MB, PETer is well-suited for deployment on edge devices, providing an efficient solution for real-time human action recognition in resource-constrained environments.
Millimeter-wave radar is increasingly being adopted for smart home systems, elder care, and surveillance monitoring, owing to its adaptability to environmental conditions, high resolution, and privacy-preserving capabilities. A key factor in effectively utilizing millimeter-wave radar is the analysis of point clouds, which are essential for recognizing human postures. However, the sparse nature of these point clouds poses significant challenges for accurate and efficient human action recognition. To overcome these issues, we present a 3D point cloud dataset tailored for human actions captured using millimeter-wave radar (mmWave-3DPCHM-1.0). This dataset is enhanced with advanced data processing techniques and cutting-edge human action recognition models. Data collection is conducted using Texas Instruments (TI)’s IWR1443-ISK and Vayyar’s vBlu radio imaging module, covering 12 common human actions, including walking, waving, standing, and falling. At the core of our approach is the Point EdgeConv and Transformer (PETer) network, which integrates edge convolution with transformer models. For each 3D point cloud frame, PETer constructs a locally directed neighborhood graph through edge convolution to extract spatial geometric features effectively. The network then leverages a series of Transformer encoding models to uncover temporal relationships across multiple point cloud frames. Extensive experiments reveal that the PETer network achieves exceptional recognition rates of 98.77% on the TI dataset and 99.51% on the Vayyar dataset, outperforming the traditional optimal baseline model by approximately 5%. With a compact model size of only 1.09 MB, PETer is well-suited for deployment on edge devices, providing an efficient solution for real-time human action recognition in resource-constrained environments.
17
2026, 15(4): 1378-1402.
Synthetic Aperture Radar (SAR) is a remote sensing technology that utilizes the principle of synthetic aperture to achieve high-resolution microwave imaging. SAR image colorization is a fundamental and crucial task in remote sensing. Unlike optical imaging, SAR imaging is unaffected by clouds and fog, enabling all-weather observation of the Earth. However, owing to its imaging principle, SAR images are grayscale images; hence, they lack spectral information and have extremely low visual clarity. Therefore, numerous studies have focused on enhancing the interpretability of SAR images by incorporating color information. This paper reviews existing SAR image colorization techniques and categorizes them into three types: Traditional SAR image colorization techniques, deep-learning-based SAR-to-Optical image colorization techniques, and SAR image colorization techniques based on radiometric property preservation. Finally, we summarize the application scenarios and future development directions.
Synthetic Aperture Radar (SAR) is a remote sensing technology that utilizes the principle of synthetic aperture to achieve high-resolution microwave imaging. SAR image colorization is a fundamental and crucial task in remote sensing. Unlike optical imaging, SAR imaging is unaffected by clouds and fog, enabling all-weather observation of the Earth. However, owing to its imaging principle, SAR images are grayscale images; hence, they lack spectral information and have extremely low visual clarity. Therefore, numerous studies have focused on enhancing the interpretability of SAR images by incorporating color information. This paper reviews existing SAR image colorization techniques and categorizes them into three types: Traditional SAR image colorization techniques, deep-learning-based SAR-to-Optical image colorization techniques, and SAR image colorization techniques based on radiometric property preservation. Finally, we summarize the application scenarios and future development directions.
18
Vortex Electromagnetic Wave Radar (VEWR) leverages the orthogonality of Orbital Angular Momentum (OAM) modes, introducing a new physical dimension that theoretically overcomes the azimuth resolution limitations of conventional radar systems and enables enhanced micro-motion perception and forward-looking imaging. However, in practical engineering applications, the limited number of available OAM modes and the presence of complex electromagnetic noise often cause severe mode aliasing and resolution degradation. Existing sparse imaging methods face inherent trade-offs between accuracy and computational efficiency and exhibit limited robustness to noise. To address these issues, this paper proposes a super-resolution imaging framework that integrates Mode Correlation Weighting and Adaptive Regularization (MCWAR). First, a forward-looking imaging geometry and a wavefront-modulated signal model for VEWR are established. Subsequently, an OAM mode correlation matrix is designed to characterize the nonuniform distribution of radiation energy among modes, where Bessel-function-modulated weights reinforce the low-rank constraints of dominant radiation components. Finally, a compound optimization model combining sparsity and low-rankness priors is developed, incorporating an adaptive weighting mechanism that dynamically balances structural preservation and noise suppression. A joint optimization framework based on the Alternating Direction Method of Multipliers (ADMM) and Augmented Lagrange Multiplier (ALM) algorithms is constructed, in which the core image-updating subproblem is efficiently solved using a momentum-accelerated Two-Dimensional Conjugate Gradient Least Squares (2D-CGLS) method. Numerical simulations and electromagnetic experiments verify that the proposed method preserves target structural integrity under limited modes and strong noise, while effectively improving both computational efficiency and imaging quality.
Vortex Electromagnetic Wave Radar (VEWR) leverages the orthogonality of Orbital Angular Momentum (OAM) modes, introducing a new physical dimension that theoretically overcomes the azimuth resolution limitations of conventional radar systems and enables enhanced micro-motion perception and forward-looking imaging. However, in practical engineering applications, the limited number of available OAM modes and the presence of complex electromagnetic noise often cause severe mode aliasing and resolution degradation. Existing sparse imaging methods face inherent trade-offs between accuracy and computational efficiency and exhibit limited robustness to noise. To address these issues, this paper proposes a super-resolution imaging framework that integrates Mode Correlation Weighting and Adaptive Regularization (MCWAR). First, a forward-looking imaging geometry and a wavefront-modulated signal model for VEWR are established. Subsequently, an OAM mode correlation matrix is designed to characterize the nonuniform distribution of radiation energy among modes, where Bessel-function-modulated weights reinforce the low-rank constraints of dominant radiation components. Finally, a compound optimization model combining sparsity and low-rankness priors is developed, incorporating an adaptive weighting mechanism that dynamically balances structural preservation and noise suppression. A joint optimization framework based on the Alternating Direction Method of Multipliers (ADMM) and Augmented Lagrange Multiplier (ALM) algorithms is constructed, in which the core image-updating subproblem is efficiently solved using a momentum-accelerated Two-Dimensional Conjugate Gradient Least Squares (2D-CGLS) method. Numerical simulations and electromagnetic experiments verify that the proposed method preserves target structural integrity under limited modes and strong noise, while effectively improving both computational efficiency and imaging quality.
19
2026, 15(4): 1451-1466.
Interference identification is a critical component in enhancing the anti-jamming capability of radar target recognition systems. Compared with single-type interference, composite interference poses substantially greater identification challenges due to its structural complexity and flexible combination patterns. However, most existing identification methods are purely data-driven and fail to incorporate interference prior knowledge, resulting in performance bottlenecks in complex scenarios and limited interpretability. Moreover, many approaches lack effective noise suppression mechanisms and are prone to noise overfitting under low Signal-to-Noise Ratio (SNR) conditions. To address these limitations, this study proposes a prior-guided, noise-robust multi-label recognition network for radar composite interference, which exploits time-domain symmetry priors in different interference types. First, a coarse-to-fine denoising strategy is employed to suppress noise while preserving and enhancing their prior structural characteristics, thereby alleviating noise-induced overfitting during the recognition process. Second, an autocorrelation-based symmetry score is introduced to quantify the strength of the interference prior. The score is then mapped into a gating mechanism via a symmetry encoder to guide interference feature fusion and temporal representation learning. Finally, noise intensity and temporal features are jointly embedded into the recognition network, further enhancing the robustness of the proposed method across varying SNR conditions. Experimental results demonstrate that, under low-SNR conditions, the proposed method achieves average recognition accuracies exceeding 90% for 15 types of intermittent sampling repeater composite interference and 30 types of complex composite interference. Moreover, the proposed approach outperforms the strongest baseline model while significantly reducing model parameters.
Interference identification is a critical component in enhancing the anti-jamming capability of radar target recognition systems. Compared with single-type interference, composite interference poses substantially greater identification challenges due to its structural complexity and flexible combination patterns. However, most existing identification methods are purely data-driven and fail to incorporate interference prior knowledge, resulting in performance bottlenecks in complex scenarios and limited interpretability. Moreover, many approaches lack effective noise suppression mechanisms and are prone to noise overfitting under low Signal-to-Noise Ratio (SNR) conditions. To address these limitations, this study proposes a prior-guided, noise-robust multi-label recognition network for radar composite interference, which exploits time-domain symmetry priors in different interference types. First, a coarse-to-fine denoising strategy is employed to suppress noise while preserving and enhancing their prior structural characteristics, thereby alleviating noise-induced overfitting during the recognition process. Second, an autocorrelation-based symmetry score is introduced to quantify the strength of the interference prior. The score is then mapped into a gating mechanism via a symmetry encoder to guide interference feature fusion and temporal representation learning. Finally, noise intensity and temporal features are jointly embedded into the recognition network, further enhancing the robustness of the proposed method across varying SNR conditions. Experimental results demonstrate that, under low-SNR conditions, the proposed method achieves average recognition accuracies exceeding 90% for 15 types of intermittent sampling repeater composite interference and 30 types of complex composite interference. Moreover, the proposed approach outperforms the strongest baseline model while significantly reducing model parameters.
20
2026, 15(4): 1220-1237.
Coherent Frequency Diverse Array (FDA) radar demonstrates significant potential for wide-area search tasks due to its simple system architecture, flexible beam scanning, and high transmit Degrees of Freedom (DOF). However, its inherent beam-scanning mechanism reduces dwell time in specific directions, thereby limiting the imaging range resolution when a conventional wideband waveform is used. To resolve the intrinsic contradiction between wide-area search and high-resolution imaging, this paper proposes a deep learning-based integrated search-imaging waveform design method. By leveraging the multi-DoF flexible transmission capability of coherent FDA, the proposed method customizes multidimensional transmit resources, including waveform, bandwidth, and transmit gain, for multiple Regions of Interest (ROIs) while preserving wide-coverage search performance. To address the nonconvex optimization problem with dual constraints of constant modulus and low correlation in baseband waveform design, a residual autoencoder-based optimizer is developed. This network directly learns and establishes a high-dimensional nonlinear mapping from the initial phase space to the optimized phase space that satisfies predefined performance criteria. The network efficiently generates a set of phase-coded subwaveforms exhibiting low autocorrelation sidelobes and low cross-correlation levels for multiple ROIs. Simulation results validate the effectiveness of this method, demonstrating that the designed waveforms achieve higher processing gain (compared with the narrowband searching mode) and improved imaging resolution in the designated ROIs during simultaneous search and multitarget imaging. Moreover, the autocorrelation and cross-correlation performance of the proposed method significantly outperforms that of conventional approaches, indicating that it provides an effective solution for enhancing the multitask capabilities of modern radar systems.
Coherent Frequency Diverse Array (FDA) radar demonstrates significant potential for wide-area search tasks due to its simple system architecture, flexible beam scanning, and high transmit Degrees of Freedom (DOF). However, its inherent beam-scanning mechanism reduces dwell time in specific directions, thereby limiting the imaging range resolution when a conventional wideband waveform is used. To resolve the intrinsic contradiction between wide-area search and high-resolution imaging, this paper proposes a deep learning-based integrated search-imaging waveform design method. By leveraging the multi-DoF flexible transmission capability of coherent FDA, the proposed method customizes multidimensional transmit resources, including waveform, bandwidth, and transmit gain, for multiple Regions of Interest (ROIs) while preserving wide-coverage search performance. To address the nonconvex optimization problem with dual constraints of constant modulus and low correlation in baseband waveform design, a residual autoencoder-based optimizer is developed. This network directly learns and establishes a high-dimensional nonlinear mapping from the initial phase space to the optimized phase space that satisfies predefined performance criteria. The network efficiently generates a set of phase-coded subwaveforms exhibiting low autocorrelation sidelobes and low cross-correlation levels for multiple ROIs. Simulation results validate the effectiveness of this method, demonstrating that the designed waveforms achieve higher processing gain (compared with the narrowband searching mode) and improved imaging resolution in the designated ROIs during simultaneous search and multitarget imaging. Moreover, the autocorrelation and cross-correlation performance of the proposed method significantly outperforms that of conventional approaches, indicating that it provides an effective solution for enhancing the multitask capabilities of modern radar systems.
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