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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
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.
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
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.
5
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.
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
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.
8
2020, 9(3): 554-568.
Landslide disasters occur frequently in the western mountainous regions of China and are characterized by high concealment, suddenness, and strong destructiveness. Early identification of potential disaster hazards is the most effective prevention and mitigation measure. The western mountainous areas mostly have a wide range of alpine-canyon terrain, which is hard or even impossible to reach. Moreover, traditional early identification methods, such as manual inspection, are difficult to implement in these areas. As an emerging radar remote-sensing method, Interferometric Synthetic Aperture Radar (InSAR) can efficiently and accurately identify the hidden dangers of landslides. Based on the synthetic aperture radar data of the European Space Agency’s Sentinel-1, this study used time series InSAR technology to identify the potential landslide hazards in the alpine-canyon terrain along the Yajiang-Muli County of the Yalong River; eight potential geohazards were detected. On the basis of the historical data of landslide hazards and the interpretation of optical remote sensing data, the results of early identification were verified and analyzed, and the danger level of the disaster points was evaluated. The influence of geometric distortion in InSAR technology on the early identification of potential landslides in alpine-canyon terrain was also discussed. This case study can provide powerful data and technical support for local disaster prevention and mitigation and provide ideas and references for the early identification of the hidden dangers of landslides in mountain-valley areas.
Landslide disasters occur frequently in the western mountainous regions of China and are characterized by high concealment, suddenness, and strong destructiveness. Early identification of potential disaster hazards is the most effective prevention and mitigation measure. The western mountainous areas mostly have a wide range of alpine-canyon terrain, which is hard or even impossible to reach. Moreover, traditional early identification methods, such as manual inspection, are difficult to implement in these areas. As an emerging radar remote-sensing method, Interferometric Synthetic Aperture Radar (InSAR) can efficiently and accurately identify the hidden dangers of landslides. Based on the synthetic aperture radar data of the European Space Agency’s Sentinel-1, this study used time series InSAR technology to identify the potential landslide hazards in the alpine-canyon terrain along the Yajiang-Muli County of the Yalong River; eight potential geohazards were detected. On the basis of the historical data of landslide hazards and the interpretation of optical remote sensing data, the results of early identification were verified and analyzed, and the danger level of the disaster points was evaluated. The influence of geometric distortion in InSAR technology on the early identification of potential landslides in alpine-canyon terrain was also discussed. This case study can provide powerful data and technical support for local disaster prevention and mitigation and provide ideas and references for the early identification of the hidden dangers of landslides in mountain-valley areas.
9
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.
10
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.
11
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.
12
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.
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
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.
15
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.
16
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.
17
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.
18
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.
19
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.
20
2019, 8(5): 537-557.
Cognitive radar can sense the battlefield environment and feed this information back to a transmitter by imitating the cognitive learning process of bats to enable self-adaptive detection and processing, which are vital for the future intelligent development of radar. Therein, full utilization of the prior information of the target and environment to design radar waveform for improving the performance of target detection, tracking, and anti-jamming is difficult and has been the focus of cognitive radar development. Therefore, based on different jamming environments, target models, and antenna configurations (e.g., Single Input Single Output (SISO) and Multiple Inputs Multiple Outputs (MIMO)), this study summarizes the key elements and main ideas of waveform design. Furthermore, this study lists the related literature on representativeness from the viewpoint of the use of different jamming environments and target models, aiming at providing reference and basis for cognitive waveform design research in the future.
Cognitive radar can sense the battlefield environment and feed this information back to a transmitter by imitating the cognitive learning process of bats to enable self-adaptive detection and processing, which are vital for the future intelligent development of radar. Therein, full utilization of the prior information of the target and environment to design radar waveform for improving the performance of target detection, tracking, and anti-jamming is difficult and has been the focus of cognitive radar development. Therefore, based on different jamming environments, target models, and antenna configurations (e.g., Single Input Single Output (SISO) and Multiple Inputs Multiple Outputs (MIMO)), this study summarizes the key elements and main ideas of waveform design. Furthermore, this study lists the related literature on representativeness from the viewpoint of the use of different jamming environments and target models, aiming at providing reference and basis for cognitive waveform design research in the future.
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