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2024
Vol. 13,
No. 3
Previous Issue
2024,
13(3):
501-524.
Abstract
18774KB
Weak target signal processing is the cornerstone and prerequisite for radar to achieve excellent detection performance. In complex practical applications, due to strong clutter interference, weak target signals, unclear image features, and difficult effective feature extraction, weak target detection and recognition have always been challenging in the field of radar processing. Conventional model-based processing methods do not accurately match the actual working background and target characteristics, leading to weak universality. Recently, deep learning has made significant progress in the field of radar intelligent information processing. By building deep neural networks, deep learning algorithms can automatically learn feature representations from a large amount of radar data, improving the performance of target detection and recognition. This article systematically reviews and summarizes recent research progress in the intelligent processing of weak radar targets in terms of signal processing, image processing, feature extraction, target classification, and target recognition. This article discusses noise and clutter suppression, target signal enhancement, low- and high-resolution radar image and feature processing, feature extraction, and fusion. In response to the limited generalization ability, single feature expression, and insufficient interpretability of existing intelligent processing applications for weak targets, this article underscores future developments from the aspects of small sample object detection (based on transfer learning and reinforcement learning), multidimensional and multifeature fusion, network model interpretability, and joint knowledge- and data-driven processing.
Weak target signal processing is the cornerstone and prerequisite for radar to achieve excellent detection performance. In complex practical applications, due to strong clutter interference, weak target signals, unclear image features, and difficult effective feature extraction, weak target detection and recognition have always been challenging in the field of radar processing. Conventional model-based processing methods do not accurately match the actual working background and target characteristics, leading to weak universality. Recently, deep learning has made significant progress in the field of radar intelligent information processing. By building deep neural networks, deep learning algorithms can automatically learn feature representations from a large amount of radar data, improving the performance of target detection and recognition. This article systematically reviews and summarizes recent research progress in the intelligent processing of weak radar targets in terms of signal processing, image processing, feature extraction, target classification, and target recognition. This article discusses noise and clutter suppression, target signal enhancement, low- and high-resolution radar image and feature processing, feature extraction, and fusion. In response to the limited generalization ability, single feature expression, and insufficient interpretability of existing intelligent processing applications for weak targets, this article underscores future developments from the aspects of small sample object detection (based on transfer learning and reinforcement learning), multidimensional and multifeature fusion, network model interpretability, and joint knowledge- and data-driven processing.
2024,
13(3):
525-538.
Fine terrain classification is one of the main applications of Synthetic Aperture Radar (SAR). In the multiband fully polarized SAR operating mode, obtaining information on different frequency bands of the target and polarization response characteristics of a target is possible, which can improve target classification accuracy. However, the existing datasets at home and abroad only have low-resolution fully polarized classification data for individual bands, limited regions, and small samples. Thus, a multidimensional SAR dataset from Hainan is used to construct a multiband fully polarized fine classification dataset with ample sample size, diverse land cover categories, and high classification reliability. This dataset will promote the development of multiband fully polarized SAR classification applications, supported by the high-resolution aerial observation system application calibration and verification project. This paper provides an overview of the composition of the dataset, and describes the information and dataset production methods for the first batch of published data (MPOLSAR-1.0). Furthermore, this study presents the preliminary classification experimental results based on the polarization feature classification and classical machine learning classification methods, providing support for the sharing and application of the dataset.
Fine terrain classification is one of the main applications of Synthetic Aperture Radar (SAR). In the multiband fully polarized SAR operating mode, obtaining information on different frequency bands of the target and polarization response characteristics of a target is possible, which can improve target classification accuracy. However, the existing datasets at home and abroad only have low-resolution fully polarized classification data for individual bands, limited regions, and small samples. Thus, a multidimensional SAR dataset from Hainan is used to construct a multiband fully polarized fine classification dataset with ample sample size, diverse land cover categories, and high classification reliability. This dataset will promote the development of multiband fully polarized SAR classification applications, supported by the high-resolution aerial observation system application calibration and verification project. This paper provides an overview of the composition of the dataset, and describes the information and dataset production methods for the first batch of published data (MPOLSAR-1.0). Furthermore, this study presents the preliminary classification experimental results based on the polarization feature classification and classical machine learning classification methods, providing support for the sharing and application of the dataset.
2024,
13(3):
539-553.
Detection of small, slow-moving targets, such as drones using Unmanned Aerial Vehicles (UAVs) poses considerable challenges to radar target detection and recognition technology. There is an urgent need to establish relevant datasets to support the development and application of techniques for detecting small, slow-moving targets. This paper presents a dataset for detecting low-speed and small-size targets using a multiband Frequency Modulated Continuous Wave (FMCW) radar. The dataset utilizes Ku-band and L-band FMCW radar to collect echo data from six UAV types and exhibits diverse temporal and frequency domain resolutions and measurement capabilities by modulating radar cycles and bandwidth, generating an LSS-FMCWR-1.0 dataset (Low Slow Small, LSS). To further enhance the capability for extracting micro-Doppler features from UAVs, this paper proposes a method for UAV micro-Doppler extraction and parameter estimation based on the local maximum synchroextracting transform. Based on the Short Time Fourier Transform (STFT), this method extracts values at the maximum energy point in the time-frequency domain to retain useful signals and refine the time-frequency energy representation. Validation and analysis using the LSS-FMCWR-1.0 dataset demonstrate that this approach reduces entropy on an average by 5.3 dB and decreases estimation errors in rotor blade length by 27.7% compared with traditional time-frequency methods. Moreover, the proposed method provides the foundation for subsequent target recognition efforts because it balances high time-frequency resolution and parameter estimation capabilities.
Detection of small, slow-moving targets, such as drones using Unmanned Aerial Vehicles (UAVs) poses considerable challenges to radar target detection and recognition technology. There is an urgent need to establish relevant datasets to support the development and application of techniques for detecting small, slow-moving targets. This paper presents a dataset for detecting low-speed and small-size targets using a multiband Frequency Modulated Continuous Wave (FMCW) radar. The dataset utilizes Ku-band and L-band FMCW radar to collect echo data from six UAV types and exhibits diverse temporal and frequency domain resolutions and measurement capabilities by modulating radar cycles and bandwidth, generating an LSS-FMCWR-1.0 dataset (Low Slow Small, LSS). To further enhance the capability for extracting micro-Doppler features from UAVs, this paper proposes a method for UAV micro-Doppler extraction and parameter estimation based on the local maximum synchroextracting transform. Based on the Short Time Fourier Transform (STFT), this method extracts values at the maximum energy point in the time-frequency domain to retain useful signals and refine the time-frequency energy representation. Validation and analysis using the LSS-FMCWR-1.0 dataset demonstrate that this approach reduces entropy on an average by 5.3 dB and decreases estimation errors in rotor blade length by 27.7% compared with traditional time-frequency methods. Moreover, the proposed method provides the foundation for subsequent target recognition efforts because it balances high time-frequency resolution and parameter estimation capabilities.
2024,
13(3):
554-564.
Considering the problem of radar target detection in the sea clutter environment, this paper proposes a deep learning-based marine target detector. The proposed detector increases the differences between the target and clutter by fusing multiple complementary features extracted from different data sources, thereby improving the detection performance for marine targets. Specifically, the detector uses two feature extraction branches to extract multiple levels of fast-time and range features from the range profiles and the range-Doppler (RD) spectrum, respectively. Subsequently, the local-global feature extraction structure is developed to extract the sequence relations from the slow time or Doppler dimension of the features. Furthermore, the feature fusion block is proposed based on adaptive convolution weight learning to efficiently fuse slow-fast time and RD features. Finally, the detection results are obtained through upsampling and nonlinear mapping to the fused multiple levels of features. Experiments on two public radar databases validated the detection performance of the proposed detector.
Considering the problem of radar target detection in the sea clutter environment, this paper proposes a deep learning-based marine target detector. The proposed detector increases the differences between the target and clutter by fusing multiple complementary features extracted from different data sources, thereby improving the detection performance for marine targets. Specifically, the detector uses two feature extraction branches to extract multiple levels of fast-time and range features from the range profiles and the range-Doppler (RD) spectrum, respectively. Subsequently, the local-global feature extraction structure is developed to extract the sequence relations from the slow time or Doppler dimension of the features. Furthermore, the feature fusion block is proposed based on adaptive convolution weight learning to efficiently fuse slow-fast time and RD features. Finally, the detection results are obtained through upsampling and nonlinear mapping to the fused multiple levels of features. Experiments on two public radar databases validated the detection performance of the proposed detector.
2024,
13(3):
565-583.
In this study, a collaborative radar selection and transmit resource allocation strategy is proposed for multitarget tracking applications in multiple distributed phased array radar networks with imperfect detection performance. The closed-form expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with imperfect detection performance is obtained and adopted as the criterion function to characterize the precision of target state estimates. The key concept of the developed strategy is to collaboratively adjust the radar node selection, transmitted power, and effective bandwidth allocation of multiple distributed phased array radar networks to minimize the total transmit power consumption in an imperfect detection environment. This will be achieved under the constraints of the predetermined tracking accuracy requirements of multiple targets and several illumination resource budgets to improve its radio frequency stealth performance. The results revealed that the formulated problem is a mixed-integer programming, nonlinear, and nonconvex optimization model. By incorporating the barrier function approach and cyclic minimization technique, an efficient four-step-based solution methodology is proposed to solve the resulting optimization problem. The numerical simulation examples demonstrate that the proposed strategy can effectively reduce the total power consumption of multiple distributed phased array radar networks by at least 32.3% and improve its radio frequency stealth performance while meeting the given multitarget tracking accuracy requirements compared with other existing algorithms.
In this study, a collaborative radar selection and transmit resource allocation strategy is proposed for multitarget tracking applications in multiple distributed phased array radar networks with imperfect detection performance. The closed-form expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with imperfect detection performance is obtained and adopted as the criterion function to characterize the precision of target state estimates. The key concept of the developed strategy is to collaboratively adjust the radar node selection, transmitted power, and effective bandwidth allocation of multiple distributed phased array radar networks to minimize the total transmit power consumption in an imperfect detection environment. This will be achieved under the constraints of the predetermined tracking accuracy requirements of multiple targets and several illumination resource budgets to improve its radio frequency stealth performance. The results revealed that the formulated problem is a mixed-integer programming, nonlinear, and nonconvex optimization model. By incorporating the barrier function approach and cyclic minimization technique, an efficient four-step-based solution methodology is proposed to solve the resulting optimization problem. The numerical simulation examples demonstrate that the proposed strategy can effectively reduce the total power consumption of multiple distributed phased array radar networks by at least 32.3% and improve its radio frequency stealth performance while meeting the given multitarget tracking accuracy requirements compared with other existing algorithms.
2024,
13(3):
584-600.
Distributed radar with moving platforms can enhance the survivability and detection performance of a system, however, it is difficult to equip these platforms with sufficient communication bandwidth to transmit high-precision observed data, posing a great challenge to the high-performance detection of a distributed radar system. Because low-bit quantization can effectively reduce the computation cost and resource consumption of distributed radar systems, in this paper, we investigate the high-performance detection of multiple moving targets using the distributed radar system on moving platforms by adopting the low-bit quantization strategy. First, according to system resources, multipulse observed data of each node may be quantized with a low-bit quantizer and the likelihood function relative to the quantizer and states of multiple targets are derived. Subsequently, based on the convexity of the likelihood function relative to the unknown reflection coefficients, a joint estimation algorithm is designed for the Doppler shifts and reflection coefficients. Then, a generalized likelihood ratio test based multi-target detector is designed for detecting multiple targets in the surveillance area with unknown states, and deriving the constant false alarm rate detection threshold. Finally, the optimal low-bit quantizer is designed by deriving the asymptotic detection performance of the system, which effectively improves the detection performance and ensures robustness. Simulation experiments are conducted to analyze the detection and estimation performance of the proposed algorithm, thereby demonstrating the effectiveness of the proposed algorithm for weak signals, and showing that the low-bit quantized data can achieve detection and estimation performance close to that of the high-precision (16-bit quantization) data while consuming a complementary 20% of the communication bandwidth. Besides, according to the simulated results, the two-bit quantization strategy may be a trade-off between the detection performance and resource consumption of the distributed radar system.
Distributed radar with moving platforms can enhance the survivability and detection performance of a system, however, it is difficult to equip these platforms with sufficient communication bandwidth to transmit high-precision observed data, posing a great challenge to the high-performance detection of a distributed radar system. Because low-bit quantization can effectively reduce the computation cost and resource consumption of distributed radar systems, in this paper, we investigate the high-performance detection of multiple moving targets using the distributed radar system on moving platforms by adopting the low-bit quantization strategy. First, according to system resources, multipulse observed data of each node may be quantized with a low-bit quantizer and the likelihood function relative to the quantizer and states of multiple targets are derived. Subsequently, based on the convexity of the likelihood function relative to the unknown reflection coefficients, a joint estimation algorithm is designed for the Doppler shifts and reflection coefficients. Then, a generalized likelihood ratio test based multi-target detector is designed for detecting multiple targets in the surveillance area with unknown states, and deriving the constant false alarm rate detection threshold. Finally, the optimal low-bit quantizer is designed by deriving the asymptotic detection performance of the system, which effectively improves the detection performance and ensures robustness. Simulation experiments are conducted to analyze the detection and estimation performance of the proposed algorithm, thereby demonstrating the effectiveness of the proposed algorithm for weak signals, and showing that the low-bit quantized data can achieve detection and estimation performance close to that of the high-precision (16-bit quantization) data while consuming a complementary 20% of the communication bandwidth. Besides, according to the simulated results, the two-bit quantization strategy may be a trade-off between the detection performance and resource consumption of the distributed radar system.
A Track Initiation Method for FM-based Passive Radar Network Based on Multiple Elementary Hypotheses
2024,
13(3):
601-612.
Passive radars based on FM radio signals have low detection probability, high false alarm rates and poor accuracy, presenting considerable challenges to target tracking in radar networks. Moreover, a high false alarm rate increases the computational burden and puts forward high requirements for the real-time performance of networking algorithms. In addition, low detection probability and poor azimuth accuracy result in a lack of redundant information, making measurement association and track initiation challenging. To address these issues, this paper proposes an FM-based passive radar network based on the concepts of elementary hypothesis points and elementary hypothesis track, as well as a track initiation algorithm. First, we construct possible low-dimensional association hypotheses and solve for their corresponding elementary hypothesis points. Subsequently, we associate elementary hypothesis points from different frames to form multiple possible elementary hypothesis tracks. Finally, by combining multi-frame radar network data for hypothesis track judgment, we confirm the elementary hypothesis tracks corresponding to the real targets, and eliminate the false elementary hypothesis tracks caused by incorrect associations. Result reveal that the proposed algorithm has lower computational complexity and faster track initiation speed than existing algorithms. Moreover, we verified the effectiveness of the proposed algorithm using simulation and experimental results.
Passive radars based on FM radio signals have low detection probability, high false alarm rates and poor accuracy, presenting considerable challenges to target tracking in radar networks. Moreover, a high false alarm rate increases the computational burden and puts forward high requirements for the real-time performance of networking algorithms. In addition, low detection probability and poor azimuth accuracy result in a lack of redundant information, making measurement association and track initiation challenging. To address these issues, this paper proposes an FM-based passive radar network based on the concepts of elementary hypothesis points and elementary hypothesis track, as well as a track initiation algorithm. First, we construct possible low-dimensional association hypotheses and solve for their corresponding elementary hypothesis points. Subsequently, we associate elementary hypothesis points from different frames to form multiple possible elementary hypothesis tracks. Finally, by combining multi-frame radar network data for hypothesis track judgment, we confirm the elementary hypothesis tracks corresponding to the real targets, and eliminate the false elementary hypothesis tracks caused by incorrect associations. Result reveal that the proposed algorithm has lower computational complexity and faster track initiation speed than existing algorithms. Moreover, we verified the effectiveness of the proposed algorithm using simulation and experimental results.
2024,
13(3):
613-628.
The modern radar confrontation situation is complex and changeable, and inter-system combat has become a basic feature. The overall system performance affects the initiative on the battlefield and even the final victory or defeat. By optimizing the beam resources of radar and jammers in a system, the overall performance can be improved, and the effective low-intercept detection effect can be obtained in the spatial and temporal domains. However, joint optimization of cooperative beamforming in the spatial and temporal domains is a nonconvex problem with complex multiparameter coupling. In this paper, an optimization model is established for a multitasking dynamic scene in the spatial and temporal domains. Radar detection performance is the optimization goal, while the interference performance and energy limitation of jammers are the constraints. To solve the model, a joint design method of space-time cooperative beamforming based on iterative optimization was proposed; that is, iterative optimization of radar transmitting, receiving, and multiple jammers transmitting beamforming vectors was alternately optimized. To solve the Quadratically Constrained Quadratic Programs (QCQP) problem with indefinite matrices for multijammer collaborative optimization, this paper is based on the Feasible Point Pursuit Successive Convex Approximation (FPP-SCA) algorithm. In other words, on the basis of the SCA algorithm, algorithm feasibility is ensured through reasonable relaxation by introducing relaxation variables and a penalty term, which solves the difficulty of obtaining a feasible solution when a problem contains indefinite matrices. Simulation results show that under the constraint of certain jammer energy, the proposed method achieves the effect of multiple jammers interfering with each enemy platform in the spatial and temporal domains to cover our radar detection. This effect is achieved while ensuring high-performance radar detection of the target without interference. Compared with traditional algorithms, the collaborative interference based on the FPP-SCA algorithm exhibits a better performance in the dynamic scene.
The modern radar confrontation situation is complex and changeable, and inter-system combat has become a basic feature. The overall system performance affects the initiative on the battlefield and even the final victory or defeat. By optimizing the beam resources of radar and jammers in a system, the overall performance can be improved, and the effective low-intercept detection effect can be obtained in the spatial and temporal domains. However, joint optimization of cooperative beamforming in the spatial and temporal domains is a nonconvex problem with complex multiparameter coupling. In this paper, an optimization model is established for a multitasking dynamic scene in the spatial and temporal domains. Radar detection performance is the optimization goal, while the interference performance and energy limitation of jammers are the constraints. To solve the model, a joint design method of space-time cooperative beamforming based on iterative optimization was proposed; that is, iterative optimization of radar transmitting, receiving, and multiple jammers transmitting beamforming vectors was alternately optimized. To solve the Quadratically Constrained Quadratic Programs (QCQP) problem with indefinite matrices for multijammer collaborative optimization, this paper is based on the Feasible Point Pursuit Successive Convex Approximation (FPP-SCA) algorithm. In other words, on the basis of the SCA algorithm, algorithm feasibility is ensured through reasonable relaxation by introducing relaxation variables and a penalty term, which solves the difficulty of obtaining a feasible solution when a problem contains indefinite matrices. Simulation results show that under the constraint of certain jammer energy, the proposed method achieves the effect of multiple jammers interfering with each enemy platform in the spatial and temporal domains to cover our radar detection. This effect is achieved while ensuring high-performance radar detection of the target without interference. Compared with traditional algorithms, the collaborative interference based on the FPP-SCA algorithm exhibits a better performance in the dynamic scene.
2024,
13(3):
629-645.
To address the challenges in tracking complex maneuvering extended targets, an effective maneuvering extended target tracking method was proposed for irregularly shaped star-convex using a Transformer network. Initially, the alpha-shape algorithm was used to model the variations in the star-convex shape. In addition, a recursive approach was proposed to estimate the irregular shape of an extended target by detailed derivation in the Bayesian filtering framework. This approach accurately estimated the shape of a static star convex extended target. Moreover, through the structural redesign of the target state transition matrix and the real-time estimation of the maneuvering extended target’s state transition matrix using a transformer network, the accurate tracking of complex maneuvering targets was achieved. Furthermore, the real-time tracking of star convex maneuvering extended targets was achieved by fusing the estimated shape contours with motion states. This study focused on constructing certain complex maneuvering extended target tracking scenarios to assess the performance of the proposed method and the comprehensive estimation capabilities of the algorithm considering both shapes and motion states using multiple performance indicators.
To address the challenges in tracking complex maneuvering extended targets, an effective maneuvering extended target tracking method was proposed for irregularly shaped star-convex using a Transformer network. Initially, the alpha-shape algorithm was used to model the variations in the star-convex shape. In addition, a recursive approach was proposed to estimate the irregular shape of an extended target by detailed derivation in the Bayesian filtering framework. This approach accurately estimated the shape of a static star convex extended target. Moreover, through the structural redesign of the target state transition matrix and the real-time estimation of the maneuvering extended target’s state transition matrix using a transformer network, the accurate tracking of complex maneuvering targets was achieved. Furthermore, the real-time tracking of star convex maneuvering extended targets was achieved by fusing the estimated shape contours with motion states. This study focused on constructing certain complex maneuvering extended target tracking scenarios to assess the performance of the proposed method and the comprehensive estimation capabilities of the algorithm considering both shapes and motion states using multiple performance indicators.
2024,
13(3):
646-666.
Scanning radar angular super-resolution technology is based on the relationship between the target and antenna pattern, and a deconvolution method is used to obtain angular resolution capabilities beyond the real beam. Most current angular super-resolution methods are based on ideal distortion-free antenna patterns and do not consider pattern changes in the actual process due to the influence of factors such as radar radome, antenna measurement errors, and non-ideal platform motion. In practice, an antenna pattern often has unknown errors, which can result in reduced target resolution and even false target generation. To address this problem, this paper proposes an angular super-resolution imaging method for airborne radar with unknown antenna errors. First, based on the Total Least Square (TLS) criterion, this paper considers the effect of the pattern error matrix and derive the corresponding objective function. Second, this paper employs the iterative reweighted optimization method to solve the objective function by adopting an alternative iteration solution idea. Finally, an adaptive parameter update method is introduced for algorithm hyperparameter selection. The simulation and experimental results demonstrate that the proposed method can achieve super-resolution reconstruction even in the presence of unknown antenna errors, promoting the robustness of the super-resolution algorithm.
Scanning radar angular super-resolution technology is based on the relationship between the target and antenna pattern, and a deconvolution method is used to obtain angular resolution capabilities beyond the real beam. Most current angular super-resolution methods are based on ideal distortion-free antenna patterns and do not consider pattern changes in the actual process due to the influence of factors such as radar radome, antenna measurement errors, and non-ideal platform motion. In practice, an antenna pattern often has unknown errors, which can result in reduced target resolution and even false target generation. To address this problem, this paper proposes an angular super-resolution imaging method for airborne radar with unknown antenna errors. First, based on the Total Least Square (TLS) criterion, this paper considers the effect of the pattern error matrix and derive the corresponding objective function. Second, this paper employs the iterative reweighted optimization method to solve the objective function by adopting an alternative iteration solution idea. Finally, an adaptive parameter update method is introduced for algorithm hyperparameter selection. The simulation and experimental results demonstrate that the proposed method can achieve super-resolution reconstruction even in the presence of unknown antenna errors, promoting the robustness of the super-resolution algorithm.
2024,
13(3):
667-681.
Forward-looking imaging of airborne scanning radar is widely used in situation awareness, autonomous navigation and terrain following. When the radar is influenced by unintentional temporally sporadic electromagnetic interference or abnormal equipment performance, the echo signal contains outliers. Existing super-resolution methods can suppress outliers and improve azimuth resolution, but the real-time computing problem is not considered. In this study, we propose an airborne scanning radar super-resolution method to achieve fast forward-looking imaging when echo data are abnormal. First, we propose using the Student-t distribution to model noise. Then, the expectation-maximization method is used to estimate the parameters. Inspired by the truncated singular value decomposition method, we introduce the truncated unitary matrix into the estimation formula of the target scattering coefficient. Finally, the size of inverse matrix is reduced and the computational complexity of parameter estimation is reduced through matrix transformation. The simulation results show that the proposed method can improve the azimuth resolution of forward-looking imaging in a shorter time, and suppress outliers in echo data.
Forward-looking imaging of airborne scanning radar is widely used in situation awareness, autonomous navigation and terrain following. When the radar is influenced by unintentional temporally sporadic electromagnetic interference or abnormal equipment performance, the echo signal contains outliers. Existing super-resolution methods can suppress outliers and improve azimuth resolution, but the real-time computing problem is not considered. In this study, we propose an airborne scanning radar super-resolution method to achieve fast forward-looking imaging when echo data are abnormal. First, we propose using the Student-t distribution to model noise. Then, the expectation-maximization method is used to estimate the parameters. Inspired by the truncated singular value decomposition method, we introduce the truncated unitary matrix into the estimation formula of the target scattering coefficient. Finally, the size of inverse matrix is reduced and the computational complexity of parameter estimation is reduced through matrix transformation. The simulation results show that the proposed method can improve the azimuth resolution of forward-looking imaging in a shorter time, and suppress outliers in echo data.
2024,
13(3):
682-695.
Inverse Synthetic Aperture Radar (ISAR) images of spacecraft are composed of discrete scatterers that exhibit weak texture, high dynamics, and discontinuity. These characteristics result in sparse point clouds obtained using traditional algorithms for the Three-Dimensional (3D) reconstruction of spacecraft ISAR images. Furthermore, using point clouds to comprehensively describe the complete shape of targets is difficult, which consequently hampers the accurate extraction of the structural and pose parameters of the target. To address this problem, considering that space targets usually have specific modular structures, this paper proposes a method for abstracting parametric structural primitives from space target ISAR images to represent their 3D structures. First, the energy accumulation algorithm is used to obtain the sparse point cloud of the target from ISAR images. Subsequently, the point cloud is fitted using parameterized primitives. Finally, primitives are projected onto the ISAR imaging plane and optimized by maximizing their similarity with the target image to obtain the optimal 3D representation of the target primitives. Compared with the traditional point cloud 3D reconstruction, this method can provide a more complete description of the three-dimensional structure of the target. Meanwhile, primitive parameters obtained using this method represent the attitude and structure of the target and can directly support subsequent tasks such as target recognition and analysis. Simulation experiments demonstrate that this method can effectively achieve the 3D abstraction of space targets based on ISAR sequential images.
Inverse Synthetic Aperture Radar (ISAR) images of spacecraft are composed of discrete scatterers that exhibit weak texture, high dynamics, and discontinuity. These characteristics result in sparse point clouds obtained using traditional algorithms for the Three-Dimensional (3D) reconstruction of spacecraft ISAR images. Furthermore, using point clouds to comprehensively describe the complete shape of targets is difficult, which consequently hampers the accurate extraction of the structural and pose parameters of the target. To address this problem, considering that space targets usually have specific modular structures, this paper proposes a method for abstracting parametric structural primitives from space target ISAR images to represent their 3D structures. First, the energy accumulation algorithm is used to obtain the sparse point cloud of the target from ISAR images. Subsequently, the point cloud is fitted using parameterized primitives. Finally, primitives are projected onto the ISAR imaging plane and optimized by maximizing their similarity with the target image to obtain the optimal 3D representation of the target primitives. Compared with the traditional point cloud 3D reconstruction, this method can provide a more complete description of the three-dimensional structure of the target. Meanwhile, primitive parameters obtained using this method represent the attitude and structure of the target and can directly support subsequent tasks such as target recognition and analysis. Simulation experiments demonstrate that this method can effectively achieve the 3D abstraction of space targets based on ISAR sequential images.
2024,
13(3):
696-713.
Metasurfaces are two-dimensional artificial structures with numerous subwavelength elements arranged periodically or aperiodically. They have demonstrated their exceptional capabilities in electromagnetic wave polarization manipulation, opening new avenues for manipulating electromagnetic waves. Metasurfaces exhibiting electrically controlled reconfigurable polarization manipulation have garnered widespread research interest. These unique metasurfaces can dynamically adjust the polarization state of electromagnetic waves through real-time modification of their structure or material properties via electrical signals. This article provides a comprehensive overview of the development of metasurfaces exhibiting electrically controlled reconfigurable polarization manipulation and explores the technological advancements of metasurfaces with different transmission characteristics in the microwave region in detail. Furthermore, it delves into and anticipates the future development of this technology.
Metasurfaces are two-dimensional artificial structures with numerous subwavelength elements arranged periodically or aperiodically. They have demonstrated their exceptional capabilities in electromagnetic wave polarization manipulation, opening new avenues for manipulating electromagnetic waves. Metasurfaces exhibiting electrically controlled reconfigurable polarization manipulation have garnered widespread research interest. These unique metasurfaces can dynamically adjust the polarization state of electromagnetic waves through real-time modification of their structure or material properties via electrical signals. This article provides a comprehensive overview of the development of metasurfaces exhibiting electrically controlled reconfigurable polarization manipulation and explores the technological advancements of metasurfaces with different transmission characteristics in the microwave region in detail. Furthermore, it delves into and anticipates the future development of this technology.
2024,
13(3):
714-729.
The field of Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR) lacks effective black-box attack algorithms. Therefore, this research proposes a migration-based black-box attack algorithm by combining the idea of the Momentum Iterative Fast Gradient Sign Method (MI-FGSM). First, random speckle noise transformation is performed according to the characteristics of SAR images to alleviate model overfitting to the speckle noise and improve the generalization performance of the algorithm. Second, an AdaBelief-Nesterov optimizer is designed to rapidly find the optimal gradient descent direction, and the attack effectiveness of the algorithm is improved through a rapid convergence of the model gradient. Finally, a quasihyperbolic momentum operator is introduced to obtain a stable model gradient descent direction so that the gradient can avoid falling into a local optimum during the rapid convergence and to further enhance the success rate of black-box attacks on adversarial examples. Simulation experiments show that compared with existing adversarial attack algorithms, the proposed algorithm improves the ensemble model black-box attack success rate of mainstream SAR-ATR deep neural networks by 3%～55% and 6.0%～57.5% on the MSTAR and FUSAR-Ship datasets, respectively; the generated adversarial examples are highly concealable.
The field of Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR) lacks effective black-box attack algorithms. Therefore, this research proposes a migration-based black-box attack algorithm by combining the idea of the Momentum Iterative Fast Gradient Sign Method (MI-FGSM). First, random speckle noise transformation is performed according to the characteristics of SAR images to alleviate model overfitting to the speckle noise and improve the generalization performance of the algorithm. Second, an AdaBelief-Nesterov optimizer is designed to rapidly find the optimal gradient descent direction, and the attack effectiveness of the algorithm is improved through a rapid convergence of the model gradient. Finally, a quasihyperbolic momentum operator is introduced to obtain a stable model gradient descent direction so that the gradient can avoid falling into a local optimum during the rapid convergence and to further enhance the success rate of black-box attacks on adversarial examples. Simulation experiments show that compared with existing adversarial attack algorithms, the proposed algorithm improves the ensemble model black-box attack success rate of mainstream SAR-ATR deep neural networks by 3%～55% and 6.0%～57.5% on the MSTAR and FUSAR-Ship datasets, respectively; the generated adversarial examples are highly concealable.
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