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Editor Work2023 Vol. 12, No. 2
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2023, 12(2): 247-261.
Abstract
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During the confrontation of electronic systems, it is challenging to deal with the enemy’s comprehensive electronic weapons by simply combining electronic equipment, such as radar, communication, surveillance, and jammers. Hence, to meet the requirements of a modern war environment, the comprehensive integration of various electronic equipment is an inevitable trend. Radar and communication equipment, which are viewed as forward eyes and ears, are very similar in hardware structure and signal processing methods. In this regard, the organic union of these two is plausible. As a result, the Dual-Function Radar and Communication (DFRC) system has received a lot of attention, where integrated waveform design is one of the key scientific issues. The DFRC waveform primarily refers to the transmit waveform that realizes radar detection and information communication functions simultaneously in multiple dimensions, such as space, time, and frequency domains, through electromagnetic spectrum sharing. This paper provides a fundamental review of the radar-centric DFRC waveform design. Initially, this paper presents a brief overview of the radar-centric DFRC system’s application scenarios. Then, the progress of radar-centric integrated waveform design research is discussed. Finally, some closing remarks and potential future research directions are provided.
During the confrontation of electronic systems, it is challenging to deal with the enemy’s comprehensive electronic weapons by simply combining electronic equipment, such as radar, communication, surveillance, and jammers. Hence, to meet the requirements of a modern war environment, the comprehensive integration of various electronic equipment is an inevitable trend. Radar and communication equipment, which are viewed as forward eyes and ears, are very similar in hardware structure and signal processing methods. In this regard, the organic union of these two is plausible. As a result, the Dual-Function Radar and Communication (DFRC) system has received a lot of attention, where integrated waveform design is one of the key scientific issues. The DFRC waveform primarily refers to the transmit waveform that realizes radar detection and information communication functions simultaneously in multiple dimensions, such as space, time, and frequency domains, through electromagnetic spectrum sharing. This paper provides a fundamental review of the radar-centric DFRC waveform design. Initially, this paper presents a brief overview of the radar-centric DFRC system’s application scenarios. Then, the progress of radar-centric integrated waveform design research is discussed. Finally, some closing remarks and potential future research directions are provided.
2023, 12(2): 262-274.
Due to several advantages of the Multi-Input Multi-Output (MIMO) system in terms of waveform, space diversity, and multiplexing, the MIMO Dual Function Radar and Communication (DFRC) system, which is responsible for target detection and securing the communication by sharing the software and hardware resources, has attracted great attention. This paper addresses the MIMO DFRC system based on permutation matrix modulation and proposes a DFRC signal matrix design method based on the Alternation Direction Method of Multipliers (ADMM). By maximizing the Peak Mainlobe to Sidelobe level Ratio (PMSR) of the beampattern with the constraints of the reference codebook for both users and eavesdroppers, the system guarantees excellent detection performance along with protecting the communication information from interception. Aiming at the communication demodulation of the permutation matrix, a permutation learning demodulation method based on the Alternating Direction Penalty Method (ADPM) is proposed to improve the demodulation efficiency of the co-use waveform. Numerical simulations verify the effectiveness of the proposed methods to achieve dual function, capable of realizing multiuser communication and deriving higher PMSR compared with the existing counterparts.
Due to several advantages of the Multi-Input Multi-Output (MIMO) system in terms of waveform, space diversity, and multiplexing, the MIMO Dual Function Radar and Communication (DFRC) system, which is responsible for target detection and securing the communication by sharing the software and hardware resources, has attracted great attention. This paper addresses the MIMO DFRC system based on permutation matrix modulation and proposes a DFRC signal matrix design method based on the Alternation Direction Method of Multipliers (ADMM). By maximizing the Peak Mainlobe to Sidelobe level Ratio (PMSR) of the beampattern with the constraints of the reference codebook for both users and eavesdroppers, the system guarantees excellent detection performance along with protecting the communication information from interception. Aiming at the communication demodulation of the permutation matrix, a permutation learning demodulation method based on the Alternating Direction Penalty Method (ADPM) is proposed to improve the demodulation efficiency of the co-use waveform. Numerical simulations verify the effectiveness of the proposed methods to achieve dual function, capable of realizing multiuser communication and deriving higher PMSR compared with the existing counterparts.
2023, 12(2): 275-286.
Because Doppler resilience is limited in the existing joint design of Integrated Sensing And Communication (ISAC) waveforms, a new Doppler resilient ISAC waveform design is proposed based on a joint design. First, with the pulse train ambiguity function, a construction of the Doppler resilient pulse train is deduced, which is equivalent to designing a waveform with a very low integral sidelobe level in a correlation zone. Accordingly, to construct the Doppler resilient ISAC pulse train, an optimization problem is proposed that takes minimizing the weighted integral sidelobe level of the ISAC waveform as the objective function and takes the energy of the transmitted waveform, the peak-to-average power ratio, and the phase difference between the transmitted ISAC waveform and the communication data modulated waveform as constraints. Because the optimization problem is nonconvex, an iterative optimization algorithm based on the Majorization-Minimization (MM) framework is proposed to solve it. Numerical simulation experiments show that compared with the traditional ISAC waveform design method, the ISAC waveform proposed in this paper has higher Doppler resilience and a lower symbol error rate, and the detection performance of the ISAC system for moving targets is considerably improved without loss of communication quality.
Because Doppler resilience is limited in the existing joint design of Integrated Sensing And Communication (ISAC) waveforms, a new Doppler resilient ISAC waveform design is proposed based on a joint design. First, with the pulse train ambiguity function, a construction of the Doppler resilient pulse train is deduced, which is equivalent to designing a waveform with a very low integral sidelobe level in a correlation zone. Accordingly, to construct the Doppler resilient ISAC pulse train, an optimization problem is proposed that takes minimizing the weighted integral sidelobe level of the ISAC waveform as the objective function and takes the energy of the transmitted waveform, the peak-to-average power ratio, and the phase difference between the transmitted ISAC waveform and the communication data modulated waveform as constraints. Because the optimization problem is nonconvex, an iterative optimization algorithm based on the Majorization-Minimization (MM) framework is proposed to solve it. Numerical simulation experiments show that compared with the traditional ISAC waveform design method, the ISAC waveform proposed in this paper has higher Doppler resilience and a lower symbol error rate, and the detection performance of the ISAC system for moving targets is considerably improved without loss of communication quality.
2023, 12(2): 287-296.
Cyclic prefixes in joint radar and communication systems based on Orthogonal Frequency Division Multiplexing (OFDM) and low probability of interception lead to weak radar echo masking on the battlefield. To address this problem, a low probability of interception waveform design scheme based on Filter Bank Multi-Carrier (FBMC) with Offset Quadrature Amplitude Modulation (FBMC-OQAM) is proposed in this paper. Mathematical models for the FBMC joint radar and communication waveform, target detection probability, and communication channel capacity are established. Under the radar and communication performance constraints required by the system, a joint optimization problem of minimizing the total transmitted power of the system is designed, and the subcarrier and power allocation scheme are optimized. Furthermore, the proposed algorithm can realize adaptive transmission where the parameters of the transmitting waveform can be optimally designed for the next pulse by utilizing the measured values of the current signal and the channel state information. Moreover, the feasibility and advantages of FBMC as the radar signal are analyzed based on the average ambiguity function. Theoretical analysis and simulation experiments show that the power allocation scheme proposed in this paper can effectively reduce the total transmitted power of the system, to achieve low interception performance compared with the equal power allocation. The FBMC waveform can effectively reduce the sidelobes caused by cyclic prefixes, which improves the radar resolution and information rate.
Cyclic prefixes in joint radar and communication systems based on Orthogonal Frequency Division Multiplexing (OFDM) and low probability of interception lead to weak radar echo masking on the battlefield. To address this problem, a low probability of interception waveform design scheme based on Filter Bank Multi-Carrier (FBMC) with Offset Quadrature Amplitude Modulation (FBMC-OQAM) is proposed in this paper. Mathematical models for the FBMC joint radar and communication waveform, target detection probability, and communication channel capacity are established. Under the radar and communication performance constraints required by the system, a joint optimization problem of minimizing the total transmitted power of the system is designed, and the subcarrier and power allocation scheme are optimized. Furthermore, the proposed algorithm can realize adaptive transmission where the parameters of the transmitting waveform can be optimally designed for the next pulse by utilizing the measured values of the current signal and the channel state information. Moreover, the feasibility and advantages of FBMC as the radar signal are analyzed based on the average ambiguity function. Theoretical analysis and simulation experiments show that the power allocation scheme proposed in this paper can effectively reduce the total transmitted power of the system, to achieve low interception performance compared with the equal power allocation. The FBMC waveform can effectively reduce the sidelobes caused by cyclic prefixes, which improves the radar resolution and information rate.
2023, 12(2): 297-311.
Joint radar-communication waveform design has been the focus of intensive research in recent years. The integrated waveform based on a collocated antenna can simultaneously detect targets and communicate with multiple users in different directions. However, integrated waveforms possess poor anti-jamming properties and lack secure communication abilities, which limits their capacity to address the jamming and eavesdropping behaviors that generate at various ranges in the same beam direction. In this study, a novel joint radar-communication waveform design method based on a distributed aperture is proposed to control waveform distributions in the three-dimensional space. First, the waveform synthesis constraint is established to synthesize the desired radar and communication waveforms in designated directions. Second, the constant modulus constraint is added to each sub-aperture, following which an integrated waveform optimization model is established based on the minimum transmission power. Finally, the alternating projection algorithm is used to iteratively solve the nonconvex optimization problem. Simulation results demonstrate that the proposed method synthesizes desired waveforms at target positions and realizes three-dimensional spatial waveform manipulation.
Joint radar-communication waveform design has been the focus of intensive research in recent years. The integrated waveform based on a collocated antenna can simultaneously detect targets and communicate with multiple users in different directions. However, integrated waveforms possess poor anti-jamming properties and lack secure communication abilities, which limits their capacity to address the jamming and eavesdropping behaviors that generate at various ranges in the same beam direction. In this study, a novel joint radar-communication waveform design method based on a distributed aperture is proposed to control waveform distributions in the three-dimensional space. First, the waveform synthesis constraint is established to synthesize the desired radar and communication waveforms in designated directions. Second, the constant modulus constraint is added to each sub-aperture, following which an integrated waveform optimization model is established based on the minimum transmission power. Finally, the alternating projection algorithm is used to iteratively solve the nonconvex optimization problem. Simulation results demonstrate that the proposed method synthesizes desired waveforms at target positions and realizes three-dimensional spatial waveform manipulation.
2023, 12(2): 312-331.
As compared with sparse scalar arrays and uniform diversely polarized arrays, sparse diversely polarized arrays show respectively advantages of possessing the ability of sensing the polarization state of source signals, avoiding polarization mismatch and increasing the degrees of freedom, reducing mutual coupling, decreasing hardware cost, etc. Therefore, the comprehensive research on sparse diversely polarized array is of great importance in both theory and realistic applications. The design architecture of sparse diversely polarized arrays, which are related not only to the location of the array elements, but also to the dipole/loop type, orientation, and polarization states of the antenna elements, are more diverse than those of sparse scalar arrays. This paper summarizes the relevant researches in this field in recent years, introduces and explores the mainstream sparse diversely polarized array structure optimization approaches from three aspects: non-uniformly sparse, uniformly sparse and mixed uniformly and non-uniformly sparse. Then the future development of sparse diversely polarized arrays is discussed in terms of deep learning-based optimization approach, sparse Multiple-Input Multiple-Output (MIMO) diversely polarized array, sparse Polarimetric Frequency Diverse Array (PFDA) radar and sparse PFDA-MIMO radar, sparse polarimetric reconfigurable intelligent surface, and the application of sparse diversely polarized array in complex indoor scenes, such as smart communications in house and Industrial Internet of Things.
As compared with sparse scalar arrays and uniform diversely polarized arrays, sparse diversely polarized arrays show respectively advantages of possessing the ability of sensing the polarization state of source signals, avoiding polarization mismatch and increasing the degrees of freedom, reducing mutual coupling, decreasing hardware cost, etc. Therefore, the comprehensive research on sparse diversely polarized array is of great importance in both theory and realistic applications. The design architecture of sparse diversely polarized arrays, which are related not only to the location of the array elements, but also to the dipole/loop type, orientation, and polarization states of the antenna elements, are more diverse than those of sparse scalar arrays. This paper summarizes the relevant researches in this field in recent years, introduces and explores the mainstream sparse diversely polarized array structure optimization approaches from three aspects: non-uniformly sparse, uniformly sparse and mixed uniformly and non-uniformly sparse. Then the future development of sparse diversely polarized arrays is discussed in terms of deep learning-based optimization approach, sparse Multiple-Input Multiple-Output (MIMO) diversely polarized array, sparse Polarimetric Frequency Diverse Array (PFDA) radar and sparse PFDA-MIMO radar, sparse polarimetric reconfigurable intelligent surface, and the application of sparse diversely polarized array in complex indoor scenes, such as smart communications in house and Industrial Internet of Things.
2023, 12(2): 332-342.
Radar forward-looking imaging is important in many fields, such as precision guidance, autonomous landing, and terrain mapping. Due to the constraints of actual radar aperture, obtaining high-resolution images using the traditional forward-looking imaging method based on real beam scanning is challenging. Compared with the entire imaging scene, the objects of interest usually occupy only a small part of the area. This sparsity enables the use of Compressed Sensing(CS) to reconstruct high-resolution forward-looking images. However, the high noise in the radar echo affects the quality of the image generated by the compressed sensing method. Inspired by the low-rank property of the final image, this paper proposes a forward-looking super-resolution imaging model that combines sparse and low-rank properties. To effectively solve the dual constraint optimization problem in the proposed model, a forward-looking image reconstruction method based on an Augmented Lagrange Multiplier(ALM) within the framework of the Alternating Direction Multiplier Method(ADMM) was proposed. Finally, the experimental results from simulation and real data show that the proposed method can effectively improve the azimuth resolution of radar forward-looking imaging while also being noise-robust.
Radar forward-looking imaging is important in many fields, such as precision guidance, autonomous landing, and terrain mapping. Due to the constraints of actual radar aperture, obtaining high-resolution images using the traditional forward-looking imaging method based on real beam scanning is challenging. Compared with the entire imaging scene, the objects of interest usually occupy only a small part of the area. This sparsity enables the use of Compressed Sensing(CS) to reconstruct high-resolution forward-looking images. However, the high noise in the radar echo affects the quality of the image generated by the compressed sensing method. Inspired by the low-rank property of the final image, this paper proposes a forward-looking super-resolution imaging model that combines sparse and low-rank properties. To effectively solve the dual constraint optimization problem in the proposed model, a forward-looking image reconstruction method based on an Augmented Lagrange Multiplier(ALM) within the framework of the Alternating Direction Multiplier Method(ADMM) was proposed. Finally, the experimental results from simulation and real data show that the proposed method can effectively improve the azimuth resolution of radar forward-looking imaging while also being noise-robust.
2023, 12(2): 343-355.
Compared with narrowband Doppler radar, ultrawideband radar can simultaneously acquire the range and Doppler information of targets, which is more beneficial for behavior recognition. To improve the recognition performance of fall behavior, frequency-modulated continuous-wave ultrawideband (UWB) radar was applied to collect daily behavior and fall data of 36 subjects in two real indoor complex scenes, and a multi-scene fall detection dataset was established with various action types; the range-time, time-Doppler, and range-Doppler spectrograms of the subjects were obtained after preprocessing radar data; based on the MobileNet-V3 lightweight network, three types of deep learning fusion networks at the data level, feature level, and decision level were designed for the radar spectrograms, respectively. A statistical analysis shows that the decision level fusion method proposed in this paper can improve fall detection performance compared with those using one type of spectrogram, the data level and the feature level fusion methods (all P values by significance test method are less than 0.003). The accuracies of 5-fold cross-validation and testing in the new scene of the decision level fusion method are 0.9956 and 0.9778, respectively, which indicates the good generalization ability of the proposed method.
Compared with narrowband Doppler radar, ultrawideband radar can simultaneously acquire the range and Doppler information of targets, which is more beneficial for behavior recognition. To improve the recognition performance of fall behavior, frequency-modulated continuous-wave ultrawideband (UWB) radar was applied to collect daily behavior and fall data of 36 subjects in two real indoor complex scenes, and a multi-scene fall detection dataset was established with various action types; the range-time, time-Doppler, and range-Doppler spectrograms of the subjects were obtained after preprocessing radar data; based on the MobileNet-V3 lightweight network, three types of deep learning fusion networks at the data level, feature level, and decision level were designed for the radar spectrograms, respectively. A statistical analysis shows that the decision level fusion method proposed in this paper can improve fall detection performance compared with those using one type of spectrogram, the data level and the feature level fusion methods (all P values by significance test method are less than 0.003). The accuracies of 5-fold cross-validation and testing in the new scene of the decision level fusion method are 0.9956 and 0.9778, respectively, which indicates the good generalization ability of the proposed method.
2023, 12(2): 356-375.
Existing data-driven object detection methods use the Constant False Alarm Rate (CFAR) principle to achieve more robust detection performance using supervised learning. This study systematically proposes a data-driven target detection framework based on the measured echo data from the ground early warning radar for low-altitude slow dim target detection. This framework addresses two key problems in this field: (1) aiming at the problem that current data-driven object detection methods fail to make full use of feature representation learning to exert its advantages, a representation learning method of echo temporal dependency is proposed, and two implementations, including unsupervised- and supervised-learning are given; (2) Low-altitude slow dim targets show extreme sparsity in the radar detection range, such unevenness of target-clutter sample scale causes the trained model to seriously tilt to the clutter samples, resulting in the decision deviation. Therefore, we further propose incorporating the data balancing policy of abnormal detection into the framework. Finally, ablation experiments are performed on the measured X-band echo data for each component in the proposed framework. Experimental results completely validate the effectiveness of our echo temporal representation learning and balancing policy. Additionally, under real sequential validation, our proposed method achieves comprehensive detection performance that is superior to multiple CFAR methods.
Existing data-driven object detection methods use the Constant False Alarm Rate (CFAR) principle to achieve more robust detection performance using supervised learning. This study systematically proposes a data-driven target detection framework based on the measured echo data from the ground early warning radar for low-altitude slow dim target detection. This framework addresses two key problems in this field: (1) aiming at the problem that current data-driven object detection methods fail to make full use of feature representation learning to exert its advantages, a representation learning method of echo temporal dependency is proposed, and two implementations, including unsupervised- and supervised-learning are given; (2) Low-altitude slow dim targets show extreme sparsity in the radar detection range, such unevenness of target-clutter sample scale causes the trained model to seriously tilt to the clutter samples, resulting in the decision deviation. Therefore, we further propose incorporating the data balancing policy of abnormal detection into the framework. Finally, ablation experiments are performed on the measured X-band echo data for each component in the proposed framework. Experimental results completely validate the effectiveness of our echo temporal representation learning and balancing policy. Additionally, under real sequential validation, our proposed method achieves comprehensive detection performance that is superior to multiple CFAR methods.
2023, 12(2): 376-388.
With the development in information technology and the change of air combat mode, Radar Warning Receiver (RWR) have become indispensable electronic warfare equipment for modern fighters. To better understand the airborne RWR system, this study divides the airborne RWR architecture into two stages from the perspective of receiver system. The characteristics and components of the architecture are analyzed. Then, this study elaborates on the signal processing flow of airborne RWR, and classifies the technologies and algorithms related to signal sorting, signal identification and threat assessment. Finally, this study systematically summarizes the challenges and future demand analysis of airborne RWR in complex battlefield environments and in dealing with new radar systems.
With the development in information technology and the change of air combat mode, Radar Warning Receiver (RWR) have become indispensable electronic warfare equipment for modern fighters. To better understand the airborne RWR system, this study divides the airborne RWR architecture into two stages from the perspective of receiver system. The characteristics and components of the architecture are analyzed. Then, this study elaborates on the signal processing flow of airborne RWR, and classifies the technologies and algorithms related to signal sorting, signal identification and threat assessment. Finally, this study systematically summarizes the challenges and future demand analysis of airborne RWR in complex battlefield environments and in dealing with new radar systems.
2023, 12(2): 389-411.
Holographic staring radar is an array radar that continuously looks everywhere and performs multiple functions simultaneously instead of sequentially. First, this paper clarifies the definition of holographic staring radar and summarizes the features, performance advantages, and accompanying risks of holographic staring radar. Then, the research history and main application directions of holographic staring radar are reviewed. Next, the holographic staring radar series of Sun Yat-sen University in China is introduced. The target detection results of this holographic staring radar are given, showing the application potential of a holographic staring radar system in low-altitude target monitoring. Next, the research progress of related key technologies is examined, including system design, beam control, target detection, and parameter estimation. Finally, the development trends of holographic staring radar are discussed.
Holographic staring radar is an array radar that continuously looks everywhere and performs multiple functions simultaneously instead of sequentially. First, this paper clarifies the definition of holographic staring radar and summarizes the features, performance advantages, and accompanying risks of holographic staring radar. Then, the research history and main application directions of holographic staring radar are reviewed. Next, the holographic staring radar series of Sun Yat-sen University in China is introduced. The target detection results of this holographic staring radar are given, showing the application potential of a holographic staring radar system in low-altitude target monitoring. Next, the research progress of related key technologies is examined, including system design, beam control, target detection, and parameter estimation. Finally, the development trends of holographic staring radar are discussed.
2023, 12(2): 412-424.
Based on the obtained knowledge through ceaseless interaction with the environment and learning from the experience, cognitive radar continuously adjusts its waveform, parameters, and illumination strategies to achieve robust target tracking in complex and changing scenarios. Its waveform design has been receiving attention to improve tracking performance. In this paper, we propose a novel framework of cognitive radar waveform selection for the tracking of high-maneuvering targets. The framework considers the combination of Constant Velocity (CV), Constant Acceleration (CA), and Coordinate Turn (CT) motions. We also design Criterion-Based Optimization (CBO) and Entropy Reward Q-Learning (ERQL) methods to perform waveform selection based on this framework. To provide the optimum target tracking performance, it merges the radar and target into a closed loop, updating the broadcast waveform in real-time as the target state changes. The suggested ERQL technique achieves about the same tracking performance as the CBO while using much less processing time than the CBO, according to numerical results. The proposed ERQL method significantly increases the tracking accuracy of moving targets as compared to the fixed parameter approach.
Based on the obtained knowledge through ceaseless interaction with the environment and learning from the experience, cognitive radar continuously adjusts its waveform, parameters, and illumination strategies to achieve robust target tracking in complex and changing scenarios. Its waveform design has been receiving attention to improve tracking performance. In this paper, we propose a novel framework of cognitive radar waveform selection for the tracking of high-maneuvering targets. The framework considers the combination of Constant Velocity (CV), Constant Acceleration (CA), and Coordinate Turn (CT) motions. We also design Criterion-Based Optimization (CBO) and Entropy Reward Q-Learning (ERQL) methods to perform waveform selection based on this framework. To provide the optimum target tracking performance, it merges the radar and target into a closed loop, updating the broadcast waveform in real-time as the target state changes. The suggested ERQL technique achieves about the same tracking performance as the CBO while using much less processing time than the CBO, according to numerical results. The proposed ERQL method significantly increases the tracking accuracy of moving targets as compared to the fixed parameter approach.
2023, 12(2): 425-440.
Migratory pests are sudden outbreaks and widespread, putting national food security at risk. Entomological radar is most effective segment in monitoring insect migration, providing critical information for early warning and pest control. Traditional entomological radar can measure biological parameters such as mass and orientation using a low-resolution waveform and rotating linear polarization antenna. The new entomological radar uses a stepped chirp high-resolution waveform and instantaneous fully polarimetric system, which can considerably improve the accuracy of measuring insect biological data. However, in addition to the traditional polarization measurement errors, the stepped chirp waveform introduces new multiplicative error components to different polarization channels, resulting in a more complex imbalance between polarization channels, which requires high-precision polarization calibration. In response to the above issues, the fully polarimetric measurement model is optimized according to the characteristics of the high-resolution system, and a high-resolution system polarization error estimation method based on a sphere and a wire is proposed in this study, which can complete polarization calibration under the loose constraint of the calibrator attitude and compensate for the influence of channel inconsistency on polarization information measurement; additionally, an insect orientation estimation method based on a biological symmetry model is proposed, and the mechanism of cross-talk between polarization channels on orientation estimation is analyzed analytically. Finally, multifrequency high-resolution fully polarimetric radar (X, Ku, Ka) is used for polarimetric calibration and insect orientation measurement experiments, and the measurement error of orientation is less than 3°, which verified the feasibility and effectiveness of the proposed method.
Migratory pests are sudden outbreaks and widespread, putting national food security at risk. Entomological radar is most effective segment in monitoring insect migration, providing critical information for early warning and pest control. Traditional entomological radar can measure biological parameters such as mass and orientation using a low-resolution waveform and rotating linear polarization antenna. The new entomological radar uses a stepped chirp high-resolution waveform and instantaneous fully polarimetric system, which can considerably improve the accuracy of measuring insect biological data. However, in addition to the traditional polarization measurement errors, the stepped chirp waveform introduces new multiplicative error components to different polarization channels, resulting in a more complex imbalance between polarization channels, which requires high-precision polarization calibration. In response to the above issues, the fully polarimetric measurement model is optimized according to the characteristics of the high-resolution system, and a high-resolution system polarization error estimation method based on a sphere and a wire is proposed in this study, which can complete polarization calibration under the loose constraint of the calibrator attitude and compensate for the influence of channel inconsistency on polarization information measurement; additionally, an insect orientation estimation method based on a biological symmetry model is proposed, and the mechanism of cross-talk between polarization channels on orientation estimation is analyzed analytically. Finally, multifrequency high-resolution fully polarimetric radar (X, Ku, Ka) is used for polarimetric calibration and insect orientation measurement experiments, and the measurement error of orientation is less than 3°, which verified the feasibility and effectiveness of the proposed method.
2023, 12(2): 441-455.
Building layover detection is a crucial step in the 3D Synthetic Aperture Radar (SAR) imaging process in urban areas. It affects imaging efficiency and directly influences the final image quality. Currently, algorithms used for layover detection struggle to extract long-range global spatial characteristics and fail to fully exploit the rich features of layover in multi-channel SAR data. To address the issue of insufficient accuracy in existing layover detection algorithms to meet the requirements of urban 3D SAR imaging, this paper proposes a deep learning-powered SAR urban layover detection method that combines the advantages of the Vision Transformer (ViT) model and Convolutional Neural Network (CNN). The ViT model can efficiently extract global and long-range features through a self-attention mechanism, whereas the CNN has strong local feature extraction capabilities. Furthermore, the proposed method in this paper incorporates a module for investigating inter-channel layover features and interferometric phase layover features based on expert knowledge, which improves the accuracy and robustness of the algorithm while effectively decreasing the training pressure on the model in small-sample datasets. Finally, the proposed algorithm is tested on a self-built airborne array SAR dataset, and experimental findings revealed that the proposed algorithm achieves a detection accuracy of >94%, which is significantly higher than other layover detection algorithms, completely revealing the effectiveness of this method.
Building layover detection is a crucial step in the 3D Synthetic Aperture Radar (SAR) imaging process in urban areas. It affects imaging efficiency and directly influences the final image quality. Currently, algorithms used for layover detection struggle to extract long-range global spatial characteristics and fail to fully exploit the rich features of layover in multi-channel SAR data. To address the issue of insufficient accuracy in existing layover detection algorithms to meet the requirements of urban 3D SAR imaging, this paper proposes a deep learning-powered SAR urban layover detection method that combines the advantages of the Vision Transformer (ViT) model and Convolutional Neural Network (CNN). The ViT model can efficiently extract global and long-range features through a self-attention mechanism, whereas the CNN has strong local feature extraction capabilities. Furthermore, the proposed method in this paper incorporates a module for investigating inter-channel layover features and interferometric phase layover features based on expert knowledge, which improves the accuracy and robustness of the algorithm while effectively decreasing the training pressure on the model in small-sample datasets. Finally, the proposed algorithm is tested on a self-built airborne array SAR dataset, and experimental findings revealed that the proposed algorithm achieves a detection accuracy of >94%, which is significantly higher than other layover detection algorithms, completely revealing the effectiveness of this method.
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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