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1
2012, 1(1): 1-10.
Abstract
2369KB
Spaceborne SAR, which is a kind of initiatively microwave imaging sensor, plays an important role in gathering information with its capability of all-day and all-weather imaging, and has become an indispensable sensor for observing the earth. With the development of SAR techniques, Spaceborne SAR has been provided with the ability of High-Resolution Wide-Swath, miniaturization with low cost, bistatic and multi-mode imaging, and Ground Moving Target Indicating (GMTI), so more accurate information about the culture could be obtained with lower cost. In the meantime, more technique problems with muliti-mode, new work system and complex environment are arising and needed to be solved. The main work of this paper is discussing the current situation and the future development of Spaceborne SAR.
Spaceborne SAR, which is a kind of initiatively microwave imaging sensor, plays an important role in gathering information with its capability of all-day and all-weather imaging, and has become an indispensable sensor for observing the earth. With the development of SAR techniques, Spaceborne SAR has been provided with the ability of High-Resolution Wide-Swath, miniaturization with low cost, bistatic and multi-mode imaging, and Ground Moving Target Indicating (GMTI), so more accurate information about the culture could be obtained with lower cost. In the meantime, more technique problems with muliti-mode, new work system and complex environment are arising and needed to be solved. The main work of this paper is discussing the current situation and the future development of Spaceborne SAR.
2
2012, 1(2): 109-123.
Starting from the detection principle and characteristics of passive radar, this paper describes the development of passive radar based on the low frequency band (HF/VHF/UHF) digital broadcasting and TV signal. Based on the radio coverage ratio and technical features of digital broadcasting and TV signals, the research status in abroad, especially in Europe, is introduced at first, on experimental systems, technical parameters, and comparative experiments. Then the latest development of passive radars, in different frequency bands in China, both theory and experimental study are presented. Followed is the commentary on the key techniques and problems of Digital Broadcasting-based Passive Radar (DBPR), including the waveforms properties and its modification, reference signal extraction, multipath clutter rejection, target detection, tracking, and fusion as well as real-time signal processing. Finally, the prospects of development and application of this kind of passive radar are discussed.
Starting from the detection principle and characteristics of passive radar, this paper describes the development of passive radar based on the low frequency band (HF/VHF/UHF) digital broadcasting and TV signal. Based on the radio coverage ratio and technical features of digital broadcasting and TV signals, the research status in abroad, especially in Europe, is introduced at first, on experimental systems, technical parameters, and comparative experiments. Then the latest development of passive radars, in different frequency bands in China, both theory and experimental study are presented. Followed is the commentary on the key techniques and problems of Digital Broadcasting-based Passive Radar (DBPR), including the waveforms properties and its modification, reference signal extraction, multipath clutter rejection, target detection, tracking, and fusion as well as real-time signal processing. Finally, the prospects of development and application of this kind of passive radar are discussed.
3
2012, 1(1): 19-27.
Viewing from the interaction between external and internal causes on the time scale of history, present and future, this paper analyzes and demonstrates the developing motivation and stage characteristics of radar technology. The external causes are interpreted as target, environment and mission, and the internal causes as information acquisition pattern, realization ability and resource utilization. The fundamental law of radar development is revealed as evolving stepwise from lower into higher dimension of detection through the aromorphosis of channel configuration, viewing angle and signal dimensionality, while the main innovation strategies of radar technology are summarized as modifying information acquisition pattern, enhancing realization ability and increasing utilized resources. Furthermore, the developing trends and main characteristics of future radar technology are deduced, and proposals for promoting future innovation and development are also presented.
Viewing from the interaction between external and internal causes on the time scale of history, present and future, this paper analyzes and demonstrates the developing motivation and stage characteristics of radar technology. The external causes are interpreted as target, environment and mission, and the internal causes as information acquisition pattern, realization ability and resource utilization. The fundamental law of radar development is revealed as evolving stepwise from lower into higher dimension of detection through the aromorphosis of channel configuration, viewing angle and signal dimensionality, while the main innovation strategies of radar technology are summarized as modifying information acquisition pattern, enhancing realization ability and increasing utilized resources. Furthermore, the developing trends and main characteristics of future radar technology are deduced, and proposals for promoting future innovation and development are also presented.
4
2017, 6(2): 136-148.
Deep learning such as deep neural networks has revolutionized the computer vision area. Deep learning-based algorithms have surpassed conventional algorithms in terms of performance by a significant margin. This paper reviews our works in the application of deep convolutional neural networks to target recognition and terrain classification using the SAR image. A convolutional neural network is employed to automatically extract a hierarchic feature representation from the data, based on which the target recognition and terrain classification can be conducted. Experimental results on the MSTAR benchmark dataset reveal that deep convolutional network could achieve a state-of-the-art classification accuracy of 99% for the 10-class task. For a polarimetric SAR image classification, we propose complex-valued convolutional neural networks for complex SAR images. This algorithm achieved a state-of-the-art accuracy of 95% for the 15-class task on the Flevoland benchmark dataset.
Deep learning such as deep neural networks has revolutionized the computer vision area. Deep learning-based algorithms have surpassed conventional algorithms in terms of performance by a significant margin. This paper reviews our works in the application of deep convolutional neural networks to target recognition and terrain classification using the SAR image. A convolutional neural network is employed to automatically extract a hierarchic feature representation from the data, based on which the target recognition and terrain classification can be conducted. Experimental results on the MSTAR benchmark dataset reveal that deep convolutional network could achieve a state-of-the-art classification accuracy of 99% for the 10-class task. For a polarimetric SAR image classification, we propose complex-valued convolutional neural networks for complex SAR images. This algorithm achieved a state-of-the-art accuracy of 95% for the 15-class task on the Flevoland benchmark dataset.
5
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.
6
2016, 5(3): 320-325.
This study presents a new method of Synthetic Aperture Radar (SAR) image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network's ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.
This study presents a new method of Synthetic Aperture Radar (SAR) image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network's ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.
7
2018, 7(5): 531-547.
The technique of radar feature extraction, imaging, and recognition of target with micro-motions has become one of the most potential research directions in the field of radar target accurate recognition. In this paper, the concept of micro-motion is first introduced briefly. Subsequently, the achievements of echo modeling, feature extraction, imaging, and identification of micro-motion targets are summarized. Several typical frontier applications are then introduced. Finally, the future development trends of the research are discussed.
The technique of radar feature extraction, imaging, and recognition of target with micro-motions has become one of the most potential research directions in the field of radar target accurate recognition. In this paper, the concept of micro-motion is first introduced briefly. Subsequently, the achievements of echo modeling, feature extraction, imaging, and identification of micro-motion targets are summarized. Several typical frontier applications are then introduced. Finally, the future development trends of the research are discussed.
8
2016, 5(5): 499-516.
Sea clutter is one of the main limiting factors influencing the target detection performance of nautical radars. The physical mechanism of sea clutter is complex with an abundance of influencing factors, and the non-Gaussian as well as non-stationarity behavior is significant. Thus, research into sea clutter property cognition is complicated and has to be systematic. Based on research that concentrates on experimental data, this paper reviews and summarizes the research developments in sea clutter property cognition. It concentrates on the properties that are of most interest for target detection algorithms:amplitude distribution, spectra, correlation, and non-stationarity and nonlinearity. The main research results are also concluded. Based on this, four aspects of problems that need further exploration are highlighted and include the following:further analysis of sea clutter influencing factors; the game problem between sea clutter precision modeling and the requirements of detection algorithms; and the property cognition between radar target and sea clutter.
Sea clutter is one of the main limiting factors influencing the target detection performance of nautical radars. The physical mechanism of sea clutter is complex with an abundance of influencing factors, and the non-Gaussian as well as non-stationarity behavior is significant. Thus, research into sea clutter property cognition is complicated and has to be systematic. Based on research that concentrates on experimental data, this paper reviews and summarizes the research developments in sea clutter property cognition. It concentrates on the properties that are of most interest for target detection algorithms:amplitude distribution, spectra, correlation, and non-stationarity and nonlinearity. The main research results are also concluded. Based on this, four aspects of problems that need further exploration are highlighted and include the following:further analysis of sea clutter influencing factors; the game problem between sea clutter precision modeling and the requirements of detection algorithms; and the property cognition between radar target and sea clutter.
9
2015, 4(4): 481-489.
In this paper, the definition and the key features of Software Radar, which is a new concept, are proposed and discussed. We consider the development of modern radar system technology to be divided into three stages: Digital Radar, Software radar and Intelligent Radar, and the second stage is just commencing now. A Software Radar system should be a combination of various modern digital modular components conformed to certain software and hardware standards. Moreover, a software radar system with an open system architecture supporting to decouple application software and low level hardware would be easy to adopt user requirements-oriented developing methodology instead of traditional specific function-oriented developing methodology. Compared with traditional Digital Radar, Software Radar system can be easily reconfigured and scaled up or down to adapt to the changes of requirements and technologies. A demonstration Software Radar signal processing system, RadarLab 2.0, which has been developed by Tsinghua University, is introduced in this paper and the suggestions for the future development of Software Radar in China are also given in the conclusion.
In this paper, the definition and the key features of Software Radar, which is a new concept, are proposed and discussed. We consider the development of modern radar system technology to be divided into three stages: Digital Radar, Software radar and Intelligent Radar, and the second stage is just commencing now. A Software Radar system should be a combination of various modern digital modular components conformed to certain software and hardware standards. Moreover, a software radar system with an open system architecture supporting to decouple application software and low level hardware would be easy to adopt user requirements-oriented developing methodology instead of traditional specific function-oriented developing methodology. Compared with traditional Digital Radar, Software Radar system can be easily reconfigured and scaled up or down to adapt to the changes of requirements and technologies. A demonstration Software Radar signal processing system, RadarLab 2.0, which has been developed by Tsinghua University, is introduced in this paper and the suggestions for the future development of Software Radar in China are also given in the conclusion.
10
2019, 8(6): 852-863.
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.
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.
11
2012, 1(2): 124-135.
Circular SAR (CSAR) is a newly developed all-directional high resolution 3D imaging mode in recent years, to satisfy the demand of finer observation. The National Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences (MITL, IECAS), had the first test flight experiment in Aug. 2011 with a P-band full polarization SAR system, and successfully obtained the all-directional high resolution circular SAR image. The initial results show that CSAR technique has the encouraging potential capability in the fields of high precision mapping, disaster evaluation, resource management and the other related applications. This paper firstly makes a detailed discussion on the progress of circular SAR imaging technique, which emphases on the several airborne experiments performed these years to show CSARs attractive features, then studies and illustrates the key techniques, and finally discusses the development trends.
Circular SAR (CSAR) is a newly developed all-directional high resolution 3D imaging mode in recent years, to satisfy the demand of finer observation. The National Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences (MITL, IECAS), had the first test flight experiment in Aug. 2011 with a P-band full polarization SAR system, and successfully obtained the all-directional high resolution circular SAR image. The initial results show that CSAR technique has the encouraging potential capability in the fields of high precision mapping, disaster evaluation, resource management and the other related applications. This paper firstly makes a detailed discussion on the progress of circular SAR imaging technique, which emphases on the several airborne experiments performed these years to show CSARs attractive features, then studies and illustrates the key techniques, and finally discusses the development trends.
12
2016, 5(2): 119-131.
Radar polarimetry is an applied fundamental science field that is focused on understanding interaction processes between radar waves and targets and disclosing their mechanisms. Radar polarimetry has significant application prospects in the fields of microwave remote sensing, earth observation, meteorological measurement, battlefield reconnaissance, anti-interference, target recognition, and so on. This study briefly reviews the development history of radar polarization theory and technology. Next, the state of the art of several key technologies within radar polarimetry, including the precise acquisition of radar polarization information, polarization-sensitive array signal processing, target polarization characteristics, polarization antiinterference, and target polarization classification and recognition, is summarized. Finally, the future developments of radar polarization technology are considered.
Radar polarimetry is an applied fundamental science field that is focused on understanding interaction processes between radar waves and targets and disclosing their mechanisms. Radar polarimetry has significant application prospects in the fields of microwave remote sensing, earth observation, meteorological measurement, battlefield reconnaissance, anti-interference, target recognition, and so on. This study briefly reviews the development history of radar polarization theory and technology. Next, the state of the art of several key technologies within radar polarimetry, including the precise acquisition of radar polarization information, polarization-sensitive array signal processing, target polarization characteristics, polarization antiinterference, and target polarization classification and recognition, is summarized. Finally, the future developments of radar polarization technology are considered.
13
2018, 7(2): 153-166.
Unlike the conventional phased array that provides only angle-dependent transmit beampattern, Frequency Diverse Array (FDA) employs a small frequency increment across its array elements to produce automatic beam scanning without requiring phase shifters or mechanical steering. FDA can produce both range-dependent and time-variant transmit beampatterns, which overcomes the disadvantages of conventional phased arrays that produce only angle-dependent beampattern. Thus, FDA has many promising applications. Based on a previous study conducted by the author, " Frequency Diverse Array Radar: Concept, Principle and Application” (Journal of Electronics & Information Technology, 2016, 38(4): 1000–1011), the current study introduces basic FDA radar concepts, principles, and application characteristics and reviews recent advances on FDA radar and its applications. In addition, several new promising applications of FDA technology are discussed, such as radar electronic warfare and radar-communications, as well as open technical challenges such as beampattern variance, effective receiver design, adaptive signal detection and estimation, and the implementation of practical FDA radar demos.
Unlike the conventional phased array that provides only angle-dependent transmit beampattern, Frequency Diverse Array (FDA) employs a small frequency increment across its array elements to produce automatic beam scanning without requiring phase shifters or mechanical steering. FDA can produce both range-dependent and time-variant transmit beampatterns, which overcomes the disadvantages of conventional phased arrays that produce only angle-dependent beampattern. Thus, FDA has many promising applications. Based on a previous study conducted by the author, " Frequency Diverse Array Radar: Concept, Principle and Application” (Journal of Electronics & Information Technology, 2016, 38(4): 1000–1011), the current study introduces basic FDA radar concepts, principles, and application characteristics and reviews recent advances on FDA radar and its applications. In addition, several new promising applications of FDA technology are discussed, such as radar electronic warfare and radar-communications, as well as open technical challenges such as beampattern variance, effective receiver design, adaptive signal detection and estimation, and the implementation of practical FDA radar demos.
14
2018, 7(5): 565-574.
In this paper, Convolutional Neural Networks (CNN) are used to detect and classify micro-Doppler effects of maritime targets by using generalized learning ability for high-dimensional features. Based on the micro-motion model of maritime targets, two-dimensional time-frequency maps of four types of micro-motion signals are constructed in the measured sea clutter background. These maps were used as training and test datasets. Furthermore, three types of CNN models, i.e., LeNet, AlexNet, and GoogleNet, are used in binary detection and multiple micro-motion classifications. The effects of signal-to-noise ratio on detection and classification performance are also studied. Compared with the traditional support vector machine method, the proposed method can learn the micro-motion features intelligently, and has performed better in detection and classification. Thus, this study can provide a new technical approach for radar target detection and recognition under a cluttered background.
In this paper, Convolutional Neural Networks (CNN) are used to detect and classify micro-Doppler effects of maritime targets by using generalized learning ability for high-dimensional features. Based on the micro-motion model of maritime targets, two-dimensional time-frequency maps of four types of micro-motion signals are constructed in the measured sea clutter background. These maps were used as training and test datasets. Furthermore, three types of CNN models, i.e., LeNet, AlexNet, and GoogleNet, are used in binary detection and multiple micro-motion classifications. The effects of signal-to-noise ratio on detection and classification performance are also studied. Compared with the traditional support vector machine method, the proposed method can learn the micro-motion features intelligently, and has performed better in detection and classification. Thus, this study can provide a new technical approach for radar target detection and recognition under a cluttered background.
15
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.
16
2017, 6(2): 195-203.
In the field of image processing using Synthetic Aperture Radar (SAR), aircraft detection is a challenging task. Conventional approaches always extract targets from the background of an image using image segmentation methods. Nevertheless, these methods mainly focus on pixel contrast and neglect the integrity of the target, which leads to locating the object inaccurately. In this study, we build a novel SAR aircraft detection framework. Compared to traditional methods, an improved saliency-based method is proposed to locate candidates coarsely and quickly in large scenes. This proposed method is verified to be more efficient compared with the sliding window method. Next, we design a convolutional neural network fitting in SAR images to accurately identify the candidates and obtain the final detection result. Moreover, to overcome the problem of limited available SAR data, we propose four data augmentation methods comprising translation, speckle noising, contrast enhancement, and small-angle rotation. Experimental results show that our framework achieves excellent performance on the high-resolution TerraSAR-X dataset.
In the field of image processing using Synthetic Aperture Radar (SAR), aircraft detection is a challenging task. Conventional approaches always extract targets from the background of an image using image segmentation methods. Nevertheless, these methods mainly focus on pixel contrast and neglect the integrity of the target, which leads to locating the object inaccurately. In this study, we build a novel SAR aircraft detection framework. Compared to traditional methods, an improved saliency-based method is proposed to locate candidates coarsely and quickly in large scenes. This proposed method is verified to be more efficient compared with the sliding window method. Next, we design a convolutional neural network fitting in SAR images to accurately identify the candidates and obtain the final detection result. Moreover, to overcome the problem of limited available SAR data, we propose four data augmentation methods comprising translation, speckle noising, contrast enhancement, and small-angle rotation. Experimental results show that our framework achieves excellent performance on the high-resolution TerraSAR-X dataset.
17
2018, 7(2): 167-182.
The carrier frequencies of array elements in a Frequency Diverse Array (FDA) radar are slightly distinguished, leading to a range-angle-time-dependent transmit beampattern. Thus, an FDA radar carries additional information in a certain range and provides more flexibility in signal processing and new technical issues. FDA is covered by scope of the general waveform diversity concept. This paper overviews the state-of-the-art FDA technology and its radar applications. From the viewpoint of the general radar system theory, we mainly introduce the coherent FDA and orthogonal FDA frameworks. The orthogonal FDA is also referred to as Multiple-Input Multiple-Output (MIMO) radar using FDA or FDA-MIMO radar. Key applications in anti-jamming and issues related with range ambiguity are addressed. We also outline the challenges in FDA radar applications and several interesting research topics.
The carrier frequencies of array elements in a Frequency Diverse Array (FDA) radar are slightly distinguished, leading to a range-angle-time-dependent transmit beampattern. Thus, an FDA radar carries additional information in a certain range and provides more flexibility in signal processing and new technical issues. FDA is covered by scope of the general waveform diversity concept. This paper overviews the state-of-the-art FDA technology and its radar applications. From the viewpoint of the general radar system theory, we mainly introduce the coherent FDA and orthogonal FDA frameworks. The orthogonal FDA is also referred to as Multiple-Input Multiple-Output (MIMO) radar using FDA or FDA-MIMO radar. Key applications in anti-jamming and issues related with range ambiguity are addressed. We also outline the challenges in FDA radar applications and several interesting research topics.
18
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.
19
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.
20
2017, 6(2): 115-135.
Automatic Target Recognition (ATR) is one of the most difficult problems in Synthetic Aperture Radar (SAR) data interpretation. In recent years, the model-based SAR target recognition method has attracted much attention because of its good performance in the extended operation condition. Based on the research of a few domestic research institutes, this paper briefly introduces the preliminary research results and gives some thoughts about SAR ATR problem. First of all, the development of parametric scattering model are discussed from three aspects. Next, two ways to model the parametric electromagnetic scattering for complex target are put forward. Finally, we propose a new framework for a Three-Dimensional (3D) parametric scattering model based SAR ATR. In the end, the future research direction of model-based SAR target recognition is prospected.
Automatic Target Recognition (ATR) is one of the most difficult problems in Synthetic Aperture Radar (SAR) data interpretation. In recent years, the model-based SAR target recognition method has attracted much attention because of its good performance in the extended operation condition. Based on the research of a few domestic research institutes, this paper briefly introduces the preliminary research results and gives some thoughts about SAR ATR problem. First of all, the development of parametric scattering model are discussed from three aspects. Next, two ways to model the parametric electromagnetic scattering for complex target are put forward. Finally, we propose a new framework for a Three-Dimensional (3D) parametric scattering model based SAR ATR. In the end, the future research direction of model-based SAR target recognition is prospected.
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