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1
2019, 8(6): 852-863.
Abstract
2711KBOver the recent years, deep-learning technology has been widely used. However, in research based on Synthetic Aperture Radar (SAR) ship target detection, it is difficult to support the training of a deep-learning network model because of the difficulty in data acquisition and the small scale of the samples. This paper provides a SAR ship detection dataset with a high resolution and large-scale images. This dataset comprises 31 images from Gaofen-3 satellite SAR images, including harbors, islands, reefs, and the sea surface in different conditions. The backgrounds include various scenarios such as the near shore and open sea. We conducted experiments using both traditional detection algorithms and deep-learning algorithms and observed the densely connected end-to-end neural network to achieve the highest average precision of 88.1%. Based on the experiments and performance analysis, corresponding benchmarks are provided as a basis for further research on SAR ship detection using this dataset.
Over the recent years, deep-learning technology has been widely used. However, in research based on Synthetic Aperture Radar (SAR) ship target detection, it is difficult to support the training of a deep-learning network model because of the difficulty in data acquisition and the small scale of the samples. This paper provides a SAR ship detection dataset with a high resolution and large-scale images. This dataset comprises 31 images from Gaofen-3 satellite SAR images, including harbors, islands, reefs, and the sea surface in different conditions. The backgrounds include various scenarios such as the near shore and open sea. We conducted experiments using both traditional detection algorithms and deep-learning algorithms and observed the densely connected end-to-end neural network to achieve the highest average precision of 88.1%. Based on the experiments and performance analysis, corresponding benchmarks are provided as a basis for further research on SAR ship detection using this dataset.
2
2019, 8(5): 656-667.
To meet the radar data requirements of target detection technology research and address the lack of publicly available sea-detecting radar data, a data-sharing program for sea-detecting radar is proposed herein. The aim of the proposed data-sharing program is to conduct sea detection experiments using an X-band solidstate phase-coherent radar and other multi-type radars to obtain the target and sea clutter data under different sea conditions, resolutions, and grazing angles. Moreover, the marine meteorological and hydrological data, target position, and track data are simultaneously obtained using the proposed data-sharing program to help achieve the standardized management of radar-measured data. The proposed data-sharing program aims to promote the open sharing of data sets, serve as the basis for research on sea clutter characteristics, and facilitate the research on sea clutter suppression and target detection technology.
To meet the radar data requirements of target detection technology research and address the lack of publicly available sea-detecting radar data, a data-sharing program for sea-detecting radar is proposed herein. The aim of the proposed data-sharing program is to conduct sea detection experiments using an X-band solidstate phase-coherent radar and other multi-type radars to obtain the target and sea clutter data under different sea conditions, resolutions, and grazing angles. Moreover, the marine meteorological and hydrological data, target position, and track data are simultaneously obtained using the proposed data-sharing program to help achieve the standardized management of radar-measured data. The proposed data-sharing program aims to promote the open sharing of data sets, serve as the basis for research on sea clutter characteristics, and facilitate the research on sea clutter suppression and target detection technology.
3
2020, 9(1): 1-33.
Spaceborne Synthetic Aperture Radar (SAR), which can be mounted on space vehicles to collect information of the entire planet with all-day and all-weather imaging capacity, has been an indispensable device for earth observation. Currently, the technology of our spaceborne SAR has achieved a considerable technological improvement, including the resolution change from meter to submeter, the imaging mode from stripmap to azimuth beam steering like the sliding spotlight, the practical application of the multichannel approach and the conversion of single polarization into full polarization. With the development of SAR techniques, forthcoming SAR will make breakthroughs in SAR architectures, concepts, technologies and modes, for example, high-resolution wide-swath imaging, multistatic SAR, payload miniaturization and intelligence. All of these will extend the observation dimensions and obtain multidimensional data. This study focuses on the forthcoming development of spaceborne SAR.
Spaceborne Synthetic Aperture Radar (SAR), which can be mounted on space vehicles to collect information of the entire planet with all-day and all-weather imaging capacity, has been an indispensable device for earth observation. Currently, the technology of our spaceborne SAR has achieved a considerable technological improvement, including the resolution change from meter to submeter, the imaging mode from stripmap to azimuth beam steering like the sliding spotlight, the practical application of the multichannel approach and the conversion of single polarization into full polarization. With the development of SAR techniques, forthcoming SAR will make breakthroughs in SAR architectures, concepts, technologies and modes, for example, high-resolution wide-swath imaging, multistatic SAR, payload miniaturization and intelligence. All of these will extend the observation dimensions and obtain multidimensional data. This study focuses on the forthcoming development of spaceborne SAR.
4
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.
5
2019, 8(6): 693-709.
Synthetic Aperture Radar three-dimensional (SAR 3D) imaging technology can eliminate severe overlap in 2D images, and improve target recognition and 3D modeling capabilities, which have become an important trend in SAR development. After decades of development of SAR 3D imaging technology, many types of 3D imaging methods have been proposed. In this study, the history of SAR 3D imaging technology is systematically reviewed and the characteristics of existing SAR 3D imaging technology are analyzed. Given that the 3D information contained in SAR echo and images is not fully used by existing techniques, a new concept of SAR microwave vision 3D imaging has been proposed for the first time. This new concept is integrated with microwave scattering mechanism and image visual semantics to realize three-dimensional reconstruction, which form the theory and method of SAR microwave vision 3D imaging and can achieve high-efficiency and low-cost SAR 3D imaging. This study also analyzes the concept, goal and key scientific problems of SAR microwave vision 3D imaging and provides a preliminary solution, which will contribute in several ways to our understanding of SAR 3D imaging and provide the basis for further research.
Synthetic Aperture Radar three-dimensional (SAR 3D) imaging technology can eliminate severe overlap in 2D images, and improve target recognition and 3D modeling capabilities, which have become an important trend in SAR development. After decades of development of SAR 3D imaging technology, many types of 3D imaging methods have been proposed. In this study, the history of SAR 3D imaging technology is systematically reviewed and the characteristics of existing SAR 3D imaging technology are analyzed. Given that the 3D information contained in SAR echo and images is not fully used by existing techniques, a new concept of SAR microwave vision 3D imaging has been proposed for the first time. This new concept is integrated with microwave scattering mechanism and image visual semantics to realize three-dimensional reconstruction, which form the theory and method of SAR microwave vision 3D imaging and can achieve high-efficiency and low-cost SAR 3D imaging. This study also analyzes the concept, goal and key scientific problems of SAR microwave vision 3D imaging and provides a preliminary solution, which will contribute in several ways to our understanding of SAR 3D imaging and provide the basis for further research.
6
2021, 10(3): 467-484.
The need of extra wireless spectrum is on the rise, given the rapid development of global wireless communication industry. To this end, Radar and Communication Spectrum Sharing (RCSS) has gained considerable attentions recently from both industry and academia. In particular, RCSS aims not only at enabling the spectral cohabitation of radar and communication systems, but also at designing a novel joint system that is capable of both functionalities. In this paper, a systematic overview of RCSS by focusing on the two main research directions are provided, i.e., Radar-Communication Coexistence (RCC) and Dual-Functional Radar-Communication (DFRC). We commence by discussing the coexistence examples of radar and communication at various frequency bands, and then elaborate on the practical application scenarios of the DFRC techniques. As a further step, the state-of-the-art approaches of both RCC and DFRC are reviewed. Finally we conclude the paper by identifying a number of open problems in the research area of RCSS.
The need of extra wireless spectrum is on the rise, given the rapid development of global wireless communication industry. To this end, Radar and Communication Spectrum Sharing (RCSS) has gained considerable attentions recently from both industry and academia. In particular, RCSS aims not only at enabling the spectral cohabitation of radar and communication systems, but also at designing a novel joint system that is capable of both functionalities. In this paper, a systematic overview of RCSS by focusing on the two main research directions are provided, i.e., Radar-Communication Coexistence (RCC) and Dual-Functional Radar-Communication (DFRC). We commence by discussing the coexistence examples of radar and communication at various frequency bands, and then elaborate on the practical application scenarios of the DFRC techniques. As a further step, the state-of-the-art approaches of both RCC and DFRC are reviewed. Finally we conclude the paper by identifying a number of open problems in the research area of RCSS.
7
2021, 10(1): 173-182.
There is an urgent need for radar-measured data to tackle key technologies of radar maritime target detection. The ‘‘Sea-detecting X-band Radar and Data Acquisition Program’’, proposed in 2019, aims to obtain data through radar experiments and share them publicly. In 2020, the program continued to advance and conducted several experiments in three aspects, namely, Radar Cross-Section (RCS) calibration of radar targets, detection of sea clutter and target under different sea conditions, as well as detection and tracking of maneuvering targets in sea. The measurement data of the stainless steel sphere calibrator at different distances in radar slow-scanning mode, sea clutter in radar staring mode in different directions, sea target in radar staring mode, and marine engine speedboat in radar scanning mode are obtained. In addition, wind and wave data, data from the Automatic Identification System (AIS) of targets, visible/infrared data, and other associated sensor data are synchronously obtained.
There is an urgent need for radar-measured data to tackle key technologies of radar maritime target detection. The ‘‘Sea-detecting X-band Radar and Data Acquisition Program’’, proposed in 2019, aims to obtain data through radar experiments and share them publicly. In 2020, the program continued to advance and conducted several experiments in three aspects, namely, Radar Cross-Section (RCS) calibration of radar targets, detection of sea clutter and target under different sea conditions, as well as detection and tracking of maneuvering targets in sea. The measurement data of the stainless steel sphere calibrator at different distances in radar slow-scanning mode, sea clutter in radar staring mode in different directions, sea target in radar staring mode, and marine engine speedboat in radar scanning mode are obtained. In addition, wind and wave data, data from the Automatic Identification System (AIS) of targets, visible/infrared data, and other associated sensor data are synchronously obtained.
8
2020, 9(4): 684-714.
Radar target detection in sea clutter is of significance to both the civil and military applications. With the miniaturization and invisibility of sea targets, Small Floating Targets (SFTs) with slow speed have become the focus of radar detection. However, the detection of SFTs in the background of sea clutter has always been a challenging problem. SFTs usually have a weak Radar Cross Section (RCS) and slow speed, making them difficult to be detected in sea clutter. Traditional target detection methods exhibit poor performance in the detection of SFTs. For the detection of small and weak targets on the sea surface, a high Doppler resolution and high range resolution system (double-high system) is an effective approach to solve this problem. In the double-high system, the target echo received by the radar provides readily available and sufficient information. However, how to transform and refine this information to improve detection performance has always been a challenge to the radar industry. In recent years, as an artificial feature engineering stage for intelligent radar target detection, scholars have proposed various feature-based target detection methods based on the double-high system to alleviate the difficulty of SFT detection when relying only on energy information and to considerably improve the detection performance. To ensure that relevant radar practitioners better understand the development of this field in recent years and the future trend, this paper summarizes the difficulties of sea target detection and common target detection methods, analyzes the principle and general framework of feature detection and several typical feature-based detection methods, and explores the development trend of feature-based detection methods.
Radar target detection in sea clutter is of significance to both the civil and military applications. With the miniaturization and invisibility of sea targets, Small Floating Targets (SFTs) with slow speed have become the focus of radar detection. However, the detection of SFTs in the background of sea clutter has always been a challenging problem. SFTs usually have a weak Radar Cross Section (RCS) and slow speed, making them difficult to be detected in sea clutter. Traditional target detection methods exhibit poor performance in the detection of SFTs. For the detection of small and weak targets on the sea surface, a high Doppler resolution and high range resolution system (double-high system) is an effective approach to solve this problem. In the double-high system, the target echo received by the radar provides readily available and sufficient information. However, how to transform and refine this information to improve detection performance has always been a challenge to the radar industry. In recent years, as an artificial feature engineering stage for intelligent radar target detection, scholars have proposed various feature-based target detection methods based on the double-high system to alleviate the difficulty of SFT detection when relying only on energy information and to considerably improve the detection performance. To ensure that relevant radar practitioners better understand the development of this field in recent years and the future trend, this paper summarizes the difficulties of sea target detection and common target detection methods, analyzes the principle and general framework of feature detection and several typical feature-based detection methods, and explores the development trend of feature-based detection methods.
9
2022, 11(5): 805-829.
As a novel radar system, the Multiple-Input Multiple-Output (MIMO) radar with waveform diversity has demonstrated excellent performance in several aspects, including target detection, parameter estimation, radio frequency stealth, and anti-jamming characteristics. After nearly 20 years of in-depth research by scholars, the MIMO radar theory based on orthogonal waveforms has significantly improved. It has been widely applied in fields such as automobile-assisted driving and safety defense. In recent years, with the introduction of the concepts of electromagnetic environment perception and knowledge aid, and the application requirements of radar-active anti-jamming, radio frequency stealth, and detection-communication integration, multiple new theories and methods have been generated for the MIMO radar in system architecture, transmit waveform design, and signal processing. This paper aims to review and summarize the research works on MIMO radar published in the past 20 years, including: the principle of the orthogonal-waveform MIMO radar, its target detection performance analysis and typical applications; waveform design and characteristics of the orthogonal-waveform MIMO radar; knowledge-aided cognitive MIMO waveform design and algorithm; MIMO detection-communication integrated waveform design and algorithm; MIMO radar parameter estimation; MIMO radar target detection; and MIMO radar resource management and scheduling. Finally, the paper discusses the clutter suppression and Space-Time Adaptive Processing (STAP) of MIMO radar in airborne applications, the signal processing of MIMO radar in imaging, and the signal processing of chirp millimeter-wave (mmWave) MIMO radar based on time division multi-waveform diversity.
As a novel radar system, the Multiple-Input Multiple-Output (MIMO) radar with waveform diversity has demonstrated excellent performance in several aspects, including target detection, parameter estimation, radio frequency stealth, and anti-jamming characteristics. After nearly 20 years of in-depth research by scholars, the MIMO radar theory based on orthogonal waveforms has significantly improved. It has been widely applied in fields such as automobile-assisted driving and safety defense. In recent years, with the introduction of the concepts of electromagnetic environment perception and knowledge aid, and the application requirements of radar-active anti-jamming, radio frequency stealth, and detection-communication integration, multiple new theories and methods have been generated for the MIMO radar in system architecture, transmit waveform design, and signal processing. This paper aims to review and summarize the research works on MIMO radar published in the past 20 years, including: the principle of the orthogonal-waveform MIMO radar, its target detection performance analysis and typical applications; waveform design and characteristics of the orthogonal-waveform MIMO radar; knowledge-aided cognitive MIMO waveform design and algorithm; MIMO detection-communication integrated waveform design and algorithm; MIMO radar parameter estimation; MIMO radar target detection; and MIMO radar resource management and scheduling. Finally, the paper discusses the clutter suppression and Space-Time Adaptive Processing (STAP) of MIMO radar in airborne applications, the signal processing of MIMO radar in imaging, and the signal processing of chirp millimeter-wave (mmWave) MIMO radar based on time division multi-waveform diversity.
10
2022, 11(4): 581-599.
This paper releases a rotated SAR ship detection dataset, named Rotated Ship Detection Dataset in SAR Images (RSDD-SAR), to address the problem that the existing rotated SAR ship detection datasets are not enough to meet the requirements of algorithm development and practical application. This dataset consists of 84 scenes of GF-3 data slices, 41 scenes of TerraSAR-X data slices, and 2 scenes of large uncropped images, including 7,000 slices and 10,263 ship instances of multi-observing modes, multi-polarization modes, and multi-resolutions. This dataset is effectively annotated by automatic annotation with manual correction. Meanwhile, experiments were conducted for several popular rotated object detection algorithms in optical remote sensing images and rotated ship detection algorithms in SAR images, and the one-stage algorithm S2ANet achieved the highest average precision of 90.06%. When using this dataset, scholars can reference the experimental results, and corresponding analysis can be used. Finally, this paper conducts generalization ability testing experiments on other datasets and large uncropped images to analyze and discuss the performance of the model trained on RSDD-SAR. The experimental results show that the model trained on RSDD-SAR has decent performance and confirms the application value of this dataset. The RSDD-SAR dataset is available at https://radars.ac.cn/web/data/getData?dataType=SDD-SAR .
This paper releases a rotated SAR ship detection dataset, named Rotated Ship Detection Dataset in SAR Images (RSDD-SAR), to address the problem that the existing rotated SAR ship detection datasets are not enough to meet the requirements of algorithm development and practical application. This dataset consists of 84 scenes of GF-3 data slices, 41 scenes of TerraSAR-X data slices, and 2 scenes of large uncropped images, including 7,000 slices and 10,263 ship instances of multi-observing modes, multi-polarization modes, and multi-resolutions. This dataset is effectively annotated by automatic annotation with manual correction. Meanwhile, experiments were conducted for several popular rotated object detection algorithms in optical remote sensing images and rotated ship detection algorithms in SAR images, and the one-stage algorithm S2ANet achieved the highest average precision of 90.06%. When using this dataset, scholars can reference the experimental results, and corresponding analysis can be used. Finally, this paper conducts generalization ability testing experiments on other datasets and large uncropped images to analyze and discuss the performance of the model trained on RSDD-SAR. The experimental results show that the model trained on RSDD-SAR has decent performance and confirms the application value of this dataset. The RSDD-SAR dataset is available at https://radars.ac.cn/web/data/getData?dataType=SDD-SAR .
11
2020, 9(6): 1014-1031.
Specific emitter identification is a technique of extracting the radio frequency fingerprints of the received electromagnetic signal only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of specific emitter identification have been continuously improved, and research on radio frequency fingerprinting feature extraction methods has made great progress. Based on the domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of specific emitter identification. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different radio frequency fingerprinting features and the correlation between them. It divides the existing radio frequency features into two main categories: direct measurement features and dimensionality reduction transform features, which have three levels. Finally, this paper analyzes and explores several potential research directions of fingerprint feature extraction, aiming to benefit the research and application of specific radiation source identification.
Specific emitter identification is a technique of extracting the radio frequency fingerprints of the received electromagnetic signal only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of specific emitter identification have been continuously improved, and research on radio frequency fingerprinting feature extraction methods has made great progress. Based on the domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of specific emitter identification. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different radio frequency fingerprinting features and the correlation between them. It divides the existing radio frequency features into two main categories: direct measurement features and dimensionality reduction transform features, which have three levels. Finally, this paper analyzes and explores several potential research directions of fingerprint feature extraction, aiming to benefit the research and application of specific radiation source identification.
12
2019, 8(5): 631-655.
The vortex electromagnetic wave, which carries the Orbital Angular Momentum (OAM), reflects a new degree of freedom in addition to the traditional degrees of freedom such as intensity, phase, frequency, and polarization. Theoretically, vortex electromagnetic wave, at any frequency, has an infinite number of orthogonal modes that do not interfere with each other, and in recent years, they have shown important potential applications in the fields of radar imaging, wireless communication and so on. Therefore, they have attracted considerable attention from scholars worldwide owing to their high research value and application prospects. Here, this paper mainly introduces the recent research advances on the antenna technology of vortex electromagnetic wave, including single microstrip patch antenna, array antenna, traveling wave antenna, and metasurface antenna structure. The single microstrip patch antenna is widely used owing to its simple structure and low manufacturing cost. The traveling wave antenna can generate multi-OAM mode vortex electromagnetic waves in a wide-frequency range. The array antenna is easy to design and controllably generate high-gain OAM electromagnetic waves with different modes. The metasurface antennas do not require complex feeding networks, which has the advantage of a lower profile of the antenna. Finally, we summarize these four common vortex antennas and further look forward to their future developments.
The vortex electromagnetic wave, which carries the Orbital Angular Momentum (OAM), reflects a new degree of freedom in addition to the traditional degrees of freedom such as intensity, phase, frequency, and polarization. Theoretically, vortex electromagnetic wave, at any frequency, has an infinite number of orthogonal modes that do not interfere with each other, and in recent years, they have shown important potential applications in the fields of radar imaging, wireless communication and so on. Therefore, they have attracted considerable attention from scholars worldwide owing to their high research value and application prospects. Here, this paper mainly introduces the recent research advances on the antenna technology of vortex electromagnetic wave, including single microstrip patch antenna, array antenna, traveling wave antenna, and metasurface antenna structure. The single microstrip patch antenna is widely used owing to its simple structure and low manufacturing cost. The traveling wave antenna can generate multi-OAM mode vortex electromagnetic waves in a wide-frequency range. The array antenna is easy to design and controllably generate high-gain OAM electromagnetic waves with different modes. The metasurface antennas do not require complex feeding networks, which has the advantage of a lower profile of the antenna. Finally, we summarize these four common vortex antennas and further look forward to their future developments.
13
2018, 7(4): 395-411.
Electromagnetic waves are transmitted by radars and reflected by different objects, and radar signal processing is highly significant as its analyses can lead to the acquisition of important information such as the situation and radial movement speed. Moreover, deep learning has gained much attention in several fields, and it can be utilized to implement radar signal processing. Compared with the traditional methods, deep learning can realize automatic feature extraction and yield highly accurate results; hence, in this paper, the application of deep learning algorithm in radar signal processing is studied. In addition, the study directions in radar signal processing are summarized into overfitting and interpretation. Thus, these two issues are being considered.
Electromagnetic waves are transmitted by radars and reflected by different objects, and radar signal processing is highly significant as its analyses can lead to the acquisition of important information such as the situation and radial movement speed. Moreover, deep learning has gained much attention in several fields, and it can be utilized to implement radar signal processing. Compared with the traditional methods, deep learning can realize automatic feature extraction and yield highly accurate results; hence, in this paper, the application of deep learning algorithm in radar signal processing is studied. In addition, the study directions in radar signal processing are summarized into overfitting and interpretation. Thus, these two issues are being considered.
14
2018, 7(1): 1-21.
Terahertz radar has unique advantages, including large bandwidth, high resolution, Doppler sensitivity, and anti-interference; it is a significant development in the field of target detection. Herein, the history of electronic and optical terahertz radar systems is introduced, and the current situation and latest progress pertaining to these systems are reviewed. The target characteristics of terahertz radar are summarized based on its mechanism, calculation, and measurement. Moreover, the current research status of terahertz SAR, ISAR, array, and aperture encoding imaging are discussed, and the applications of terahertz radar, such as early warning detection and security anti-terrorism systems, are briefly introduced. Finally, the development direction of terahertz radar technology is forecast.
Terahertz radar has unique advantages, including large bandwidth, high resolution, Doppler sensitivity, and anti-interference; it is a significant development in the field of target detection. Herein, the history of electronic and optical terahertz radar systems is introduced, and the current situation and latest progress pertaining to these systems are reviewed. The target characteristics of terahertz radar are summarized based on its mechanism, calculation, and measurement. Moreover, the current research status of terahertz SAR, ISAR, array, and aperture encoding imaging are discussed, and the applications of terahertz radar, such as early warning detection and security anti-terrorism systems, are briefly introduced. Finally, the development direction of terahertz radar technology is forecast.
15
2022, 11(3): 418-433.
Radar emitter signal deinterleaving is a key technology for radar signal reconnaissance and an essential part of battlefield situational awareness. This paper systematically sorts out the mainstream technology of radar emitter signal deinterleaving. It summarizes the main research progress in radar emitter signal deinterleaving from three directions: interpulse modulation characteristics-based, intrapulse modulation characteristics-based, and machine learning-based research. Particularly, this paper focuses on explaining the principle and technical characteristics of the latest deinterleaving technology, such as neural network-based and data stream clustering-based techniques. Finally, the shortcomings of the current radar emitter deinterleaving technology are summarized, and the future trend is predicted.
Radar emitter signal deinterleaving is a key technology for radar signal reconnaissance and an essential part of battlefield situational awareness. This paper systematically sorts out the mainstream technology of radar emitter signal deinterleaving. It summarizes the main research progress in radar emitter signal deinterleaving from three directions: interpulse modulation characteristics-based, intrapulse modulation characteristics-based, and machine learning-based research. Particularly, this paper focuses on explaining the principle and technical characteristics of the latest deinterleaving technology, such as neural network-based and data stream clustering-based techniques. Finally, the shortcomings of the current radar emitter deinterleaving technology are summarized, and the future trend is predicted.
16
2020, 9(3): 497-513.
Target detection and recognition are popular issues in the field of high-resolution Synthetic Aperture Radar (SAR). As a typical target, aircraft detection and identification has certain uniqueness. This paper reviews the development of detection and recognition techniques for a typical target in SAR imagery, analyzes the scattering mechanism and technical difficulties of aircraft in SAR imagery, describes the system flow, technical routes, and key scientific problems of target aircraft detection and recognition in SAR imagery, summarizes the research progress from traditional methods to deep-learning-based methods for aircraft detection and recognition, discusses the characteristics and existing problems of various methods, and predicts the future development trend. This paper proposes that combining target electromagnetic scattering mechanism with deep convolutional neural network to improve the generalization capability of the model is the key to improve SAR detection and recognition performance. Moreover, this paper establishes an aircraft detection method based on the fusion of scattering information and deep convolutional neural network.
Target detection and recognition are popular issues in the field of high-resolution Synthetic Aperture Radar (SAR). As a typical target, aircraft detection and identification has certain uniqueness. This paper reviews the development of detection and recognition techniques for a typical target in SAR imagery, analyzes the scattering mechanism and technical difficulties of aircraft in SAR imagery, describes the system flow, technical routes, and key scientific problems of target aircraft detection and recognition in SAR imagery, summarizes the research progress from traditional methods to deep-learning-based methods for aircraft detection and recognition, discusses the characteristics and existing problems of various methods, and predicts the future development trend. This paper proposes that combining target electromagnetic scattering mechanism with deep convolutional neural network to improve the generalization capability of the model is the key to improve SAR detection and recognition performance. Moreover, this paper establishes an aircraft detection method based on the fusion of scattering information and deep convolutional neural network.
17
2020, 9(1): 73-85.
In recent years, spaceborne Interferometric Synthetic Aperture Radar (InSAR) technology has shown increasing application potential in the field of geohazard monitoring. In this article, we first introduce the principle of InSAR technology, then systematically review the development of InSAR technology and analyze the technical characteristics and applicable scope of methods such as differential InSAR and time-series InSAR. We then discuss the application status and development trend of InSAR technology in geohazard monitoring with respect to earthquakes, landslides, hydropower projects, and ground subsidence. Finally, to guide future work in the dynamic monitoring and prevention of geohazards, we summarize the key issues and scientific problems faced by the application of InSAR to geohazard monitoring, which include atmospheric correction, complex-area deformation data acquisition, and the acquisition of multidimensional deformation data. Judging from the current applications of geological hazard monitoring, this technology is now at the point of extensive application. With the development of future spaceborne SAR satellite systems and the driving force of industry application, InSAR technology will develop into a sophisticated high-precision ground observation technology that will have a huge impact on geological hazard monitoring.
In recent years, spaceborne Interferometric Synthetic Aperture Radar (InSAR) technology has shown increasing application potential in the field of geohazard monitoring. In this article, we first introduce the principle of InSAR technology, then systematically review the development of InSAR technology and analyze the technical characteristics and applicable scope of methods such as differential InSAR and time-series InSAR. We then discuss the application status and development trend of InSAR technology in geohazard monitoring with respect to earthquakes, landslides, hydropower projects, and ground subsidence. Finally, to guide future work in the dynamic monitoring and prevention of geohazards, we summarize the key issues and scientific problems faced by the application of InSAR to geohazard monitoring, which include atmospheric correction, complex-area deformation data acquisition, and the acquisition of multidimensional deformation data. Judging from the current applications of geological hazard monitoring, this technology is now at the point of extensive application. With the development of future spaceborne SAR satellite systems and the driving force of industry application, InSAR technology will develop into a sophisticated high-precision ground observation technology that will have a huge impact on geological hazard monitoring.
18
2022, 11(3): 499-516.
A contactless health monitoring system can contribute to health assessment in daily life by reducing appliance usage and avoiding discomfort from wearing electrodes or sensors. Such contactless approaches have the potential to continuously monitor the health status of users, alert patients and health personnel in time when acute medical emergencies occur, and meet the monitoring demands of special populations, such as newborns, burn patients, and patients with infectious diseases. The Frequency-Modulated Continuous-Wave (FMCW) radar can measure the range and velocity of sensing targets and be widely applied in heart and respiration rate monitoring and fall detection. Moreover, advances in FMCW radar have enabled low-cost radar-on-chip and antenna-on-chip systems. Thus, FMCW radar has vital application value in the medical and health monitoring fields. In this study, first, we introduce the basic knowledge of the application of FMCW radar in contactless health monitoring. Then, we systematically review the advanced applications and latest papers in this field. Finally, we summarize the present situations and limitations and provide a brief outlook for the application prospects and potential future research in the field.
A contactless health monitoring system can contribute to health assessment in daily life by reducing appliance usage and avoiding discomfort from wearing electrodes or sensors. Such contactless approaches have the potential to continuously monitor the health status of users, alert patients and health personnel in time when acute medical emergencies occur, and meet the monitoring demands of special populations, such as newborns, burn patients, and patients with infectious diseases. The Frequency-Modulated Continuous-Wave (FMCW) radar can measure the range and velocity of sensing targets and be widely applied in heart and respiration rate monitoring and fall detection. Moreover, advances in FMCW radar have enabled low-cost radar-on-chip and antenna-on-chip systems. Thus, FMCW radar has vital application value in the medical and health monitoring fields. In this study, first, we introduce the basic knowledge of the application of FMCW radar in contactless health monitoring. Then, we systematically review the advanced applications and latest papers in this field. Finally, we summarize the present situations and limitations and provide a brief outlook for the application prospects and potential future research in the field.
19
2019, 8(1): 140-153.
Cross-eye jamming is an effective angular deception jamming technique used for countering monopulse radars. With the need of countermeasure against active radar seekers, the research on cross-eye jamming becomes a hot research topic in electronic war. This study overviews the cross-eye jamming with regard to jamming theories, equipment, application problems, and current research trends to offer comprehensive knowledge and future research ideas.
Cross-eye jamming is an effective angular deception jamming technique used for countering monopulse radars. With the need of countermeasure against active radar seekers, the research on cross-eye jamming becomes a hot research topic in electronic war. This study overviews the cross-eye jamming with regard to jamming theories, equipment, application problems, and current research trends to offer comprehensive knowledge and future research ideas.
20
2021, 10(4): 656-664.
With the advent of the aging population, fall detection has gradually become a research hotspot. Aiming at the detection of human fall using millimeter-wave radar, a Range-Doppler heat map Sequence detection Network (RDSNet) model that combines the convolutional neural network and long short-term memory network is proposed in this study. First, feature extraction is performed using the convolutional neural network. After obtaining the feature vector, the feature vector corresponding to the dynamic sequence is inputted to the long short-term memory network. Subsequently, the time correlation information of the heat map sequence is learned. Finally, the detection results are obtained using the classifier. Moreover, diverse human movement information of different objects is collected using millimeter-wave radar, and a range-Doppler heat map dataset is built in this work. Comparative experiments show that the proposed RDSNet model can reach an accuracy of 96.67% and the calculation delay is not higher than 50 ms. The proposed RDSNet model has good generalization capabilities and provides new technical ideas for human fall detection and human posture recognition.
With the advent of the aging population, fall detection has gradually become a research hotspot. Aiming at the detection of human fall using millimeter-wave radar, a Range-Doppler heat map Sequence detection Network (RDSNet) model that combines the convolutional neural network and long short-term memory network is proposed in this study. First, feature extraction is performed using the convolutional neural network. After obtaining the feature vector, the feature vector corresponding to the dynamic sequence is inputted to the long short-term memory network. Subsequently, the time correlation information of the heat map sequence is learned. Finally, the detection results are obtained using the classifier. Moreover, diverse human movement information of different objects is collected using millimeter-wave radar, and a range-Doppler heat map dataset is built in this work. Comparative experiments show that the proposed RDSNet model can reach an accuracy of 96.67% and the calculation delay is not higher than 50 ms. The proposed RDSNet model has good generalization capabilities and provides new technical ideas for human fall detection and human posture recognition.
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