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1
2024, 13(1): 1-22.
Abstract
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The Back Projection (BP) algorithm is an important direction in the development of synthetic aperture radar imaging algorithms. However, the large computational load of the BP algorithm has hindered its development in engineering applications. Therefore, techniques to enhance the computational efficiency of the BP algorithm have recently received widespread attention. This paper discusses the fast BP algorithm based on various imaging plane coordinate systems, including the distance-azimuth plane coordinate system, the ground plane coordinate system, and the non-Euclidean coordinate system. First, the principle of the original BP algorithm and the impact of different coordinate systems on accelerating the BP algorithm are introduced, and the development history of the BP algorithm is sorted out. Then, the research progress of the fast BP algorithm based on different imaging plane coordinate systems is examined, focusing on the recent research work completed by the author’s research team. Finally, the application of fast BP algorithm in engineering is introduced, and the research development trend of the fast BP imaging algorithm is discussed.
The Back Projection (BP) algorithm is an important direction in the development of synthetic aperture radar imaging algorithms. However, the large computational load of the BP algorithm has hindered its development in engineering applications. Therefore, techniques to enhance the computational efficiency of the BP algorithm have recently received widespread attention. This paper discusses the fast BP algorithm based on various imaging plane coordinate systems, including the distance-azimuth plane coordinate system, the ground plane coordinate system, and the non-Euclidean coordinate system. First, the principle of the original BP algorithm and the impact of different coordinate systems on accelerating the BP algorithm are introduced, and the development history of the BP algorithm is sorted out. Then, the research progress of the fast BP algorithm based on different imaging plane coordinate systems is examined, focusing on the recent research work completed by the author’s research team. Finally, the application of fast BP algorithm in engineering is introduced, and the research development trend of the fast BP imaging algorithm is discussed.
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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, 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 4.7 dB and decreases estimation errors in rotor blade length by 10.9% 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, 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 4.7 dB and decreases estimation errors in rotor blade length by 10.9% 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.
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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.
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2023, 12(6): 1229-1248.
Coherently combining distributed apertures adjusts the transmitted/received signals of multiple distributed small apertures, allowing coordinated distributed systems to obtain high power aperture products at much lower cost than large aperture. This is a promising and viable technology as an alternative to using large apertures. This study describes the concept and principles of coherently combining distributed apertures. Depending on whether external signal inputs at the combination destination are necessary, the implementation architecture of coherent combination is classified into two categories: closed- and open-loop. The development of coherently combining distributed apertures and their application in fields such as missile defense, deep space telemetry control, radar detection over ultralong range, and radio astronomy are then comprehensively presented. Furthermore, key techniques for aligning the time and phase of the transmitted/received signals for each aperture are elaborated, which are also necessary for coherently combining distributed apertures, including high-precision distributed time-frequency transfer and synchronization, and coherently combining parameters estimation, measurement and calibration, and prediction. Finally, summary is presented, and the scope of future works in this field is explored.
Coherently combining distributed apertures adjusts the transmitted/received signals of multiple distributed small apertures, allowing coordinated distributed systems to obtain high power aperture products at much lower cost than large aperture. This is a promising and viable technology as an alternative to using large apertures. This study describes the concept and principles of coherently combining distributed apertures. Depending on whether external signal inputs at the combination destination are necessary, the implementation architecture of coherent combination is classified into two categories: closed- and open-loop. The development of coherently combining distributed apertures and their application in fields such as missile defense, deep space telemetry control, radar detection over ultralong range, and radio astronomy are then comprehensively presented. Furthermore, key techniques for aligning the time and phase of the transmitted/received signals for each aperture are elaborated, which are also necessary for coherently combining distributed apertures, including high-precision distributed time-frequency transfer and synchronization, and coherently combining parameters estimation, measurement and calibration, and prediction. Finally, summary is presented, and the scope of future works in this field is explored.
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2023, 12(5): 923-970.
As one of the core components of Advanced Driver Assistance Systems (ADAS), automotive millimeter-wave radar has become the focus of scholars and manufacturers at home and abroad because it has the advantages of all-day and all-weather operation, miniaturization, high integration, and key sensing capabilities. The core performance indicators of the automotive millimeter-wave radar are distance, speed, angular resolution, and field of view. Accuracy, cost, real-time and detection performance, and volume are the key issues to be considered. The increasing performance requirements pose several challenges for the signal processing of millimeter-wave radar systems. Radar signal processing technology is crucial for improving radar performance to meet more stringent requirements. Obtaining dense radar point clouds, generating accurate radar imaging results, and mitigating mutual interference among multiple radar systems are the key points and the foundation for subsequent tracking, recognition, and other applications. Therefore, this paper discusses the practical application of the automotive millimeter-wave radar system based on the key technologies of signal processing, summarizes relevant research results, and mainly discusses the topics of point cloud imaging processing, synthetic aperture radar imaging processing, and interference suppression. Finally, herein, we summarize the research status at home and abroad. Moreover, future development trends for automotive millimeter-wave radar systems are forecast with the hope of enlightening readers in related fields.
As one of the core components of Advanced Driver Assistance Systems (ADAS), automotive millimeter-wave radar has become the focus of scholars and manufacturers at home and abroad because it has the advantages of all-day and all-weather operation, miniaturization, high integration, and key sensing capabilities. The core performance indicators of the automotive millimeter-wave radar are distance, speed, angular resolution, and field of view. Accuracy, cost, real-time and detection performance, and volume are the key issues to be considered. The increasing performance requirements pose several challenges for the signal processing of millimeter-wave radar systems. Radar signal processing technology is crucial for improving radar performance to meet more stringent requirements. Obtaining dense radar point clouds, generating accurate radar imaging results, and mitigating mutual interference among multiple radar systems are the key points and the foundation for subsequent tracking, recognition, and other applications. Therefore, this paper discusses the practical application of the automotive millimeter-wave radar system based on the key technologies of signal processing, summarizes relevant research results, and mainly discusses the topics of point cloud imaging processing, synthetic aperture radar imaging processing, and interference suppression. Finally, herein, we summarize the research status at home and abroad. Moreover, future development trends for automotive millimeter-wave radar systems are forecast with the hope of enlightening readers in related fields.
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2023, 12(4): 657-683.
Since the introduction of Maxwell’s equations in the 19th century, computational electromagnetics has dramatically increased development. This growth can be attributed to the evolution of numerical algorithms, such as the finite difference method, finite element method, method of moments, and high-frequency approximation methods. These numerical techniques have become a crucial foundation of modern electronic and information engineering. Artificial intelligence has recently witnessed considerable development in electromagnetics; the rapid growth within this field owes itself to its robust modeling and inferential capability. This advancement has given rise to the emerging field of intelligent electromagnetic computing, which has captured the attention of numerous researchers. Remarkable achievements include electromagnetic modeling and simulation, analysis and synthesis of new electromagnetic materials and devices, and detection and perception. These contributions have injected fresh insights into the realm of electromagnetics. This paper discusses recent advances in intelligent electromagnetic computing to highlight new perspectives and avenues in research in this emerging field.
Since the introduction of Maxwell’s equations in the 19th century, computational electromagnetics has dramatically increased development. This growth can be attributed to the evolution of numerical algorithms, such as the finite difference method, finite element method, method of moments, and high-frequency approximation methods. These numerical techniques have become a crucial foundation of modern electronic and information engineering. Artificial intelligence has recently witnessed considerable development in electromagnetics; the rapid growth within this field owes itself to its robust modeling and inferential capability. This advancement has given rise to the emerging field of intelligent electromagnetic computing, which has captured the attention of numerous researchers. Remarkable achievements include electromagnetic modeling and simulation, analysis and synthesis of new electromagnetic materials and devices, and detection and perception. These contributions have injected fresh insights into the realm of electromagnetics. This paper discusses recent advances in intelligent electromagnetic computing to highlight new perspectives and avenues in research in this emerging field.
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2024, 13(2): 307-330.
Synthetic Aperture Radar (SAR), with its coherent imaging mechanism, has the unique advantage of all-day and all-weather imaging. As a typical and important topic, aircraft detection and recognition have been widely studied in the field of SAR image interpretation. With the introduction of deep learning, the performance of aircraft detection and recognition, which is based on SAR imagery, has considerably improved. This paper combines the expertise gathered by our research team on the theory, algorithms, and applications of SAR image-based target detection and recognition, particularly aircraft. Additionally, this paper presents a comprehensive review of deep learning-powered aircraft detection and recognition based on SAR imagery. This review includes a detailed analysis of the aircraft target characteristics and current challenges associated with SAR image-based detection and recognition. Furthermore, the review summarizes the latest research advancements, characteristics, and application scenarios of various technologies and collates public datasets and performance evaluation metrics. Finally, several challenges and potential research prospects are discussed.
Synthetic Aperture Radar (SAR), with its coherent imaging mechanism, has the unique advantage of all-day and all-weather imaging. As a typical and important topic, aircraft detection and recognition have been widely studied in the field of SAR image interpretation. With the introduction of deep learning, the performance of aircraft detection and recognition, which is based on SAR imagery, has considerably improved. This paper combines the expertise gathered by our research team on the theory, algorithms, and applications of SAR image-based target detection and recognition, particularly aircraft. Additionally, this paper presents a comprehensive review of deep learning-powered aircraft detection and recognition based on SAR imagery. This review includes a detailed analysis of the aircraft target characteristics and current challenges associated with SAR image-based detection and recognition. Furthermore, the review summarizes the latest research advancements, characteristics, and application scenarios of various technologies and collates public datasets and performance evaluation metrics. Finally, several challenges and potential research prospects are discussed.
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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.
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2024, 13(2): 285-306.
With the rapid development of high-resolution radar imaging technology, artificial intelligence, and big data technology, remarkable advancements have been made in the intelligent interpretation of radar imagery. Despite growing demands, radar image intrpretation is now facing various technical challenges mainly because of the particularity of the radar sensor itself and the complexity of electromagnetic scattering physical phenomena. To address the problem of microwave radar imagery perception, this article proposes the development of the cross-disciplinary microwave vision research, which further integrates electromagnetic physics and radar imaging mechanism with human brain visual perception principles and computer vision technologies. This article discusses the concept and implication of microwave vision, proposes a microwave vision perception model, and explains its basic scientific problems and technical roadmaps. Finally, it introduces the preliminary research progress on related issues achieved by the authors’ group.
With the rapid development of high-resolution radar imaging technology, artificial intelligence, and big data technology, remarkable advancements have been made in the intelligent interpretation of radar imagery. Despite growing demands, radar image intrpretation is now facing various technical challenges mainly because of the particularity of the radar sensor itself and the complexity of electromagnetic scattering physical phenomena. To address the problem of microwave radar imagery perception, this article proposes the development of the cross-disciplinary microwave vision research, which further integrates electromagnetic physics and radar imaging mechanism with human brain visual perception principles and computer vision technologies. This article discusses the concept and implication of microwave vision, proposes a microwave vision perception model, and explains its basic scientific problems and technical roadmaps. Finally, it introduces the preliminary research progress on related issues achieved by the authors’ group.
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2024, 13(1): 87-96.
This paper proposes a novel multimodal collaborative perception framework to enhance the situational awareness of autonomous vehicles. First, a multimodal fusion baseline system is built that effectively integrates Light Detection and Ranging (LiDAR) point clouds and camera images. This system provides a comparable benchmark for subsequent research. Second, various well-known feature fusion strategies are investigated in the context of collaborative scenarios, including channel-wise concatenation, element-wise summation, and transformer-based methods. This study aims to seamlessly integrate intermediate representations from different sensor modalities, facilitating an exhaustive assessment of their effects on model performance. Extensive experiments were conducted on a large-scale open-source simulation dataset, i.e., OPV2V. The results showed that attention-based multimodal fusion outperforms alternative solutions, delivering more precise target localization during complex traffic scenarios, thereby enhancing the safety and reliability of autonomous driving systems.
This paper proposes a novel multimodal collaborative perception framework to enhance the situational awareness of autonomous vehicles. First, a multimodal fusion baseline system is built that effectively integrates Light Detection and Ranging (LiDAR) point clouds and camera images. This system provides a comparable benchmark for subsequent research. Second, various well-known feature fusion strategies are investigated in the context of collaborative scenarios, including channel-wise concatenation, element-wise summation, and transformer-based methods. This study aims to seamlessly integrate intermediate representations from different sensor modalities, facilitating an exhaustive assessment of their effects on model performance. Extensive experiments were conducted on a large-scale open-source simulation dataset, i.e., OPV2V. The results showed that attention-based multimodal fusion outperforms alternative solutions, delivering more precise target localization during complex traffic scenarios, thereby enhancing the safety and reliability of autonomous driving systems.
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2024, 13(1): 23-45.
The Multipath Exploitation Radar (MER) target detection technology is primarily based on the Non-Line-Of-Sight (NLOS) multipath propagation characteristics of electromagnetic waves, such as reflection and diffraction on the surface of the medium, enabling the effective detection of targets hidden in the “visually” blind area, such as urban street corners and vehicle occlusion. Thus, the technology can be feasible for various applications, including urban combat and intelligent driving. Further, it has significant practical and research implications. This paper summarizes the domestic and foreign literature in this field since the beginning of the 21st century to keep abreast of developments in this field and predict future development trends. The literature review revealed that according to the different types of detection platforms, MER target detection technology primarily consists of multipath detection technologies based on air and ground platforms. Both these technologies have achieved certain produced research results of practical significance. For air platforms, the following aspects are discussed: feasibility verification, analysis of influencing factors, architectural environment perception, and NLOS target detection. Further, for ground platforms, these four aspects are covered: target detection and recognition, two-dimensional target positioning, three-dimensional target information acquisition, and new detection methods. Finally, the prospects of MER target detection technology are summarized, and the potential issues and challenges in the current practical application of this technology are highlighted. These results show that MER target detection technology is evolving toward diversification and intelligence.
The Multipath Exploitation Radar (MER) target detection technology is primarily based on the Non-Line-Of-Sight (NLOS) multipath propagation characteristics of electromagnetic waves, such as reflection and diffraction on the surface of the medium, enabling the effective detection of targets hidden in the “visually” blind area, such as urban street corners and vehicle occlusion. Thus, the technology can be feasible for various applications, including urban combat and intelligent driving. Further, it has significant practical and research implications. This paper summarizes the domestic and foreign literature in this field since the beginning of the 21st century to keep abreast of developments in this field and predict future development trends. The literature review revealed that according to the different types of detection platforms, MER target detection technology primarily consists of multipath detection technologies based on air and ground platforms. Both these technologies have achieved certain produced research results of practical significance. For air platforms, the following aspects are discussed: feasibility verification, analysis of influencing factors, architectural environment perception, and NLOS target detection. Further, for ground platforms, these four aspects are covered: target detection and recognition, two-dimensional target positioning, three-dimensional target information acquisition, and new detection methods. Finally, the prospects of MER target detection technology are summarized, and the potential issues and challenges in the current practical application of this technology are highlighted. These results show that MER target detection technology is evolving toward diversification and intelligence.
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2021, 10(2): 206-219.
Recently, reconfigurable metasurfaces have attracted intense attention in the field of electromagnetic metasurfaces. Compared with other metasurfaces, reconfigurable metasurfaces that uses steerable devices or materials to control the electromagnetic wave in real time are more versatile and show great promise in engineering applications. Our team has continuously explored advances of reconfigurable metasurfaces and also studied the microwave region from the perspectives of theory, technique and applications. This study reviews the research history of reconfigurable metasurfaces and summarizes some of our previous works, including a study on the amplitude, phase and polarization modulation of electromagnetic waves and their applications. Finally, the study discusses future challenges and possibilities for reconfigurable metasurfaces.
Recently, reconfigurable metasurfaces have attracted intense attention in the field of electromagnetic metasurfaces. Compared with other metasurfaces, reconfigurable metasurfaces that uses steerable devices or materials to control the electromagnetic wave in real time are more versatile and show great promise in engineering applications. Our team has continuously explored advances of reconfigurable metasurfaces and also studied the microwave region from the perspectives of theory, technique and applications. This study reviews the research history of reconfigurable metasurfaces and summarizes some of our previous works, including a study on the amplitude, phase and polarization modulation of electromagnetic waves and their applications. Finally, the study discusses future challenges and possibilities for reconfigurable metasurfaces.
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2024, 13(1): 68-86.
Through-the-wall radar can penetrate walls and realize indoor human target detection. Deep learning is commonly used to extract the micro-Doppler signature of a target, which can be used to effectively identify human activities behind obstacles. However, the test accuracy of the deep-learning-based recognition methods is low with poor generalization ability when different testers are invited to generate the training set and test set. Therefore, this study proposes a method for recognition of anomalous human gait termination based on micro-Doppler corner features and Non-Local mechanism. In this method, Harris and Moravec detectors are utilized to extract the corner features of the radar image, and the corner feature dataset is established in this manner. Thereafter, multilink parallel convolutions and the Non-Local mechanism are utilized to construct the global contextual information extraction network to learn the global distribution characteristics of the image pixels. The semantic feature maps are generated by repeating four times the global contextual information extraction network. Finally, the probabilities of human activities are predicted using a multilayer perceptron. The numerical simulation and experimental results demonstrate that the proposed method can effectively identify such abnormal gait termination activities as sitting, lying down, and falling, among others, which occur in the process of indoor human walking, and successfully control the generalization accuracy error to be no more than \begin{document}$ 6.4\% $\end{document} ![]()
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2017, 6(6): 575-586.
Used to suppress strong clutter and jamming in airborne radar data, Space Time Adaptive Processing (STAP) is a multidimensional adaptive filtering technique that simultaneously combines signals from elements of an antenna array and multiple pulses of coherent radar waveforms. As a key technology for improving the performance of airborne radar, it has attracted much attention in the field of radar research and from powerful military nations in recent years. In this paper, the research and development status of STAP technology is reviewed including methodologies, experimental systems, and applications and we focus on the key technical problems encountered during its development. Then, the application of STAP technology in equipment is introduced. Finally, the next development trends, future directions, and areas worthy of further research are presented.
Used to suppress strong clutter and jamming in airborne radar data, Space Time Adaptive Processing (STAP) is a multidimensional adaptive filtering technique that simultaneously combines signals from elements of an antenna array and multiple pulses of coherent radar waveforms. As a key technology for improving the performance of airborne radar, it has attracted much attention in the field of radar research and from powerful military nations in recent years. In this paper, the research and development status of STAP technology is reviewed including methodologies, experimental systems, and applications and we focus on the key technical problems encountered during its development. Then, the application of STAP technology in equipment is introduced. Finally, the next development trends, future directions, and areas worthy of further research are presented.
15
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.
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2024, 13(1): 46-67.
With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.
With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.
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2023, 12(6): 1215-1228.
Automatic Target Recognition (ATR) is an interdisciplinary technological field related to pattern recognition, artificial intelligence, and information processing. ATR evaluation focuses on accessing ATR algorithms and systems. Due to the noncooperative targets, complex operating conditions, and multiple subjective preferences of the decision maker, ATR evaluation is performed for the entire ATR research process and shows its importance in guiding ATR development. This paper presents the connotation of ATR evaluation and briefly reviews ATR development. Furthermore, the conventional methods, applications, and latest developments in ATR evaluation are presented and discussed from the perspective of performance measures, test condition, inference and decision. Finally, several ATR evaluation research directions are summarized. This paper serves as a valuable reference for a better understanding of ATR evaluation and the effective adoption of various ATR evaluation methods.
Automatic Target Recognition (ATR) is an interdisciplinary technological field related to pattern recognition, artificial intelligence, and information processing. ATR evaluation focuses on accessing ATR algorithms and systems. Due to the noncooperative targets, complex operating conditions, and multiple subjective preferences of the decision maker, ATR evaluation is performed for the entire ATR research process and shows its importance in guiding ATR development. This paper presents the connotation of ATR evaluation and briefly reviews ATR development. Furthermore, the conventional methods, applications, and latest developments in ATR evaluation are presented and discussed from the perspective of performance measures, test condition, inference and decision. Finally, several ATR evaluation research directions are summarized. This paper serves as a valuable reference for a better understanding of ATR evaluation and the effective adoption of various ATR evaluation methods.
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2020, 9(5): 803-827.
Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.
Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.
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2024, 13(1): 150-159.
The traditional Direction Of Arrival (DOA) estimation is typically based on phased array antenna systems. However, it is greatly limited by the high hardware cost for applications in various fields. In addition, conventional phased array antennas also suffer from the high Radar Cross-Section (RCS), which cannot be employed for stealth purposes. To address these issues, we propose a new algorithm based on the Space-Time-coding (STC) strategy for simultaneous DOA estimation and RCS reduction, which is further experimentally verified using a metasurface in the millimeter band. The results demonstrate the excellent performance of the proposed DOA method with an error below 1°. Meanwhile, a good RCS reduction of over 10 dB is achieved in the bandwidth of interest. The proposed algorithm paves a new path to integrating DOA estimation and RCS reduction with a single metasurface, with the advantages of low cost and good performance.
The traditional Direction Of Arrival (DOA) estimation is typically based on phased array antenna systems. However, it is greatly limited by the high hardware cost for applications in various fields. In addition, conventional phased array antennas also suffer from the high Radar Cross-Section (RCS), which cannot be employed for stealth purposes. To address these issues, we propose a new algorithm based on the Space-Time-coding (STC) strategy for simultaneous DOA estimation and RCS reduction, which is further experimentally verified using a metasurface in the millimeter band. The results demonstrate the excellent performance of the proposed DOA method with an error below 1°. Meanwhile, a good RCS reduction of over 10 dB is achieved in the bandwidth of interest. The proposed algorithm paves a new path to integrating DOA estimation and RCS reduction with a single metasurface, with the advantages of low cost and good performance.
20
2023, 12(1): 197-213.
Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed.
Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed.
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