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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): 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.
3
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.
4
2019, 8(6): 732-757.
The multi-platform-borne Synthetic Aperture Radar (SAR) has become one of the most explored research directions in the domain of SAR. This study discusses the imaging algorithms in multi-platform-borne SARs such as airborne SAR, missile-borne SAR, and spaceborne SAR. First, the establishment of the radar echo model is briefly introduced, including two main points: slant range-model and imaging mode. Subsequently, the imaging algorithms of the aforementioned multi-platform-borne SARs developed and used in recent years are summarized. In addition, the inherent characteristics and challenges are described. Finally, the future development trends of the research are discussed.
The multi-platform-borne Synthetic Aperture Radar (SAR) has become one of the most explored research directions in the domain of SAR. This study discusses the imaging algorithms in multi-platform-borne SARs such as airborne SAR, missile-borne SAR, and spaceborne SAR. First, the establishment of the radar echo model is briefly introduced, including two main points: slant range-model and imaging mode. Subsequently, the imaging algorithms of the aforementioned multi-platform-borne SARs developed and used in recent years are summarized. In addition, the inherent characteristics and challenges are described. Finally, the future development trends of the research are discussed.
5
2019, 8(6): 669-692.
This paper analyzes the multi-directional evolution of radar ground imaging technology from the aspects of the representation of imaging results, aperture manifolds, signal channels, system morphologies, observation directions, processing methods, realization mechanisms, and target recognition. Attempts are made to analyze and understand the internal and external factors as well as the development law of radar ground imaging technology from a macroscopic perspective over a long time scale, and to predict the direction of future development. Alternative observation perspective and thinking method are proposed with a view to advance the understanding of the times veins and macro trends of radar ground imaging technology, meet practical needs, lead innovation efforts, and promote development and applications.
This paper analyzes the multi-directional evolution of radar ground imaging technology from the aspects of the representation of imaging results, aperture manifolds, signal channels, system morphologies, observation directions, processing methods, realization mechanisms, and target recognition. Attempts are made to analyze and understand the internal and external factors as well as the development law of radar ground imaging technology from a macroscopic perspective over a long time scale, and to predict the direction of future development. Alternative observation perspective and thinking method are proposed with a view to advance the understanding of the times veins and macro trends of radar ground imaging technology, meet practical needs, lead innovation efforts, and promote development and applications.
6
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.
7
2019, 8(1): 154-170.
Ground-Based Differential Interferometric Radars (GB-DInRads) have been widely applied to measure deformations. In this paper, the main types of GB-DInRads are summarized, including ground-based real aperture radar and Ground-Based Synthetic Aperture Radar (GB-SAR). The working principles and important parameters of some representative systems are introduced. Then, taking the GB-SAR as an example, the current key processing techniques are introduced, which mainly include differential interferometry, permanent scatterer selection, and atmospheric phase compensation. Lastly, three examples are presented to show the applications of GB-DInRad in deformation measurements. A Multiple-Input Multiple-Output (MIMO) radar was utilized to monitor an open-pit mine for about 11 days, and two regions with obvious deformation were found. In addition, a linear-scanning GB-SAR was utilized to monitor a mountain slope after severe landslide. The analysis result proved that rainfall could accelerate deformation. The feasibility of vibration measurement with the MIMO radar was also discussed in this paper.
Ground-Based Differential Interferometric Radars (GB-DInRads) have been widely applied to measure deformations. In this paper, the main types of GB-DInRads are summarized, including ground-based real aperture radar and Ground-Based Synthetic Aperture Radar (GB-SAR). The working principles and important parameters of some representative systems are introduced. Then, taking the GB-SAR as an example, the current key processing techniques are introduced, which mainly include differential interferometry, permanent scatterer selection, and atmospheric phase compensation. Lastly, three examples are presented to show the applications of GB-DInRad in deformation measurements. A Multiple-Input Multiple-Output (MIMO) radar was utilized to monitor an open-pit mine for about 11 days, and two regions with obvious deformation were found. In addition, a linear-scanning GB-SAR was utilized to monitor a mountain slope after severe landslide. The analysis result proved that rainfall could accelerate deformation. The feasibility of vibration measurement with the MIMO radar was also discussed in this paper.
8
2019, 8(5): 558-577.
Array signal processing is an essential tool in broad radar applications. The coprime array has recently been proposed to overcome the bottleneck caused by the Nyquist spatial sampling rate. The coprime array, whose sparse structure and undersampling feature drastically decrease necessary computational and hardware cost, provides a theoretical foundation and technical basis for the increasing demands of its practical applications. Considering its superior performance in degrees-of-freedom, spatial resolution, and computational complexity, research on coprime array signal processing has attracted much attention. This paper reviews recent research progress on coprime array signal processing, which has focused on both the Direction-of-Arrival (DOA) estimation and adaptive beamforming. From the perspective of coprime array DOA estimation, this paper summarizes two typical approaches, namely the coprime subarray decomposition-based approach and the virtual array signal processing-based approach. Moreover, recent work on low-complexity and super-resolution DOA estimation via compressive sensing and gridless techniques is also introduced. From the perspective of coprime array adaptive beamforming, the differences and relationships between DOA estimation and beamforming in the framework of coprime array signal processing are discussed, and an efficient, robust, and adaptive beamformer design tailored for the coprime array is introduced. Advantages and the future directions of coprime array signal processing are discussed, along with the theoretical basis and a technical reference for practical radar applications.
Array signal processing is an essential tool in broad radar applications. The coprime array has recently been proposed to overcome the bottleneck caused by the Nyquist spatial sampling rate. The coprime array, whose sparse structure and undersampling feature drastically decrease necessary computational and hardware cost, provides a theoretical foundation and technical basis for the increasing demands of its practical applications. Considering its superior performance in degrees-of-freedom, spatial resolution, and computational complexity, research on coprime array signal processing has attracted much attention. This paper reviews recent research progress on coprime array signal processing, which has focused on both the Direction-of-Arrival (DOA) estimation and adaptive beamforming. From the perspective of coprime array DOA estimation, this paper summarizes two typical approaches, namely the coprime subarray decomposition-based approach and the virtual array signal processing-based approach. Moreover, recent work on low-complexity and super-resolution DOA estimation via compressive sensing and gridless techniques is also introduced. From the perspective of coprime array adaptive beamforming, the differences and relationships between DOA estimation and beamforming in the framework of coprime array signal processing are discussed, and an efficient, robust, and adaptive beamformer design tailored for the coprime array is introduced. Advantages and the future directions of coprime array signal processing are discussed, along with the theoretical basis and a technical reference for practical radar applications.
9
2019, 8(1): 36-43.
The rapid advances in positioning technology have created huge spatio-temporal trajectory data, and there are always obvious aberrant outliers in trajectory data. Detecting outliers in the trajectory is critical to improving data quality and the accuracy of subsequent trajectory data mining tasks. In this paper, we propose a trajectory outlier detection algorithm based on a Bidirectional Long Short-Term Memory (Bi-LSTM) model. First, a six-dimensional motion feature vector is extracted for each trajectory point, and then we construct a Bi-LSTM model. The model input is the trajectory data feature vector of a certain sequence length, and its output is the class type of the current track point. In addition, a combination method of undersampling and oversampling is applied to mitigate the effect of data distribution imbalance on detection performance. The Bi-LSTM model can automatically learn the difference between the normal points and adjacent abnormal points in the motion characteristics by combining the LSTM unit and the bidirectional network. Experimental results based on a real ship trajectory annotation data show that the detection performance of our proposed algorithm significantly exceeds those of the constant velocity threshold algorithm, non-sequential classical machine learning classification algorithms, and convolutional neural network model. Especially, the recall value of the proposed algorithm reaches 0.902, which verifies its effectiveness.
The rapid advances in positioning technology have created huge spatio-temporal trajectory data, and there are always obvious aberrant outliers in trajectory data. Detecting outliers in the trajectory is critical to improving data quality and the accuracy of subsequent trajectory data mining tasks. In this paper, we propose a trajectory outlier detection algorithm based on a Bidirectional Long Short-Term Memory (Bi-LSTM) model. First, a six-dimensional motion feature vector is extracted for each trajectory point, and then we construct a Bi-LSTM model. The model input is the trajectory data feature vector of a certain sequence length, and its output is the class type of the current track point. In addition, a combination method of undersampling and oversampling is applied to mitigate the effect of data distribution imbalance on detection performance. The Bi-LSTM model can automatically learn the difference between the normal points and adjacent abnormal points in the motion characteristics by combining the LSTM unit and the bidirectional network. Experimental results based on a real ship trajectory annotation data show that the detection performance of our proposed algorithm significantly exceeds those of the constant velocity threshold algorithm, non-sequential classical machine learning classification algorithms, and convolutional neural network model. Especially, the recall value of the proposed algorithm reaches 0.902, which verifies its effectiveness.
10
2019, 8(1): 100-106.
Through partial intercepting and multiple forwarding of a radar transmitting signal, Digital Radio Frequency Memory (DRFM)-based Interrupted Sampling Repeater Jamming (ISRJ) possesses advantages of small size, light weight, and flexibility. Thus, DRFM-ISRJ can be equipped on targets to perform multi-point source main-lobe jamming, posing a serious threat to modern radars. In this study, a time-frequency domain recognition and suppression method was analyzed. First, the expression of pulse compression and Time-Frequency Distribution (TFD) of the jamming signal were deduced. Then, the differences of TFD between target echo and jamming signal were analyzed. On this basis, a jamming recognition program and a time-frequency domain filter to suppress the jamming were proposed. Simulation results show that the recognition rate is better than 90% when the jamming-to-noise ratio is over –3 dB for the received signal. Based on correct recognition, a signal to jamming-and-noise ratio improvement of 18 dB can be achieved using the time-frequency filter.
Through partial intercepting and multiple forwarding of a radar transmitting signal, Digital Radio Frequency Memory (DRFM)-based Interrupted Sampling Repeater Jamming (ISRJ) possesses advantages of small size, light weight, and flexibility. Thus, DRFM-ISRJ can be equipped on targets to perform multi-point source main-lobe jamming, posing a serious threat to modern radars. In this study, a time-frequency domain recognition and suppression method was analyzed. First, the expression of pulse compression and Time-Frequency Distribution (TFD) of the jamming signal were deduced. Then, the differences of TFD between target echo and jamming signal were analyzed. On this basis, a jamming recognition program and a time-frequency domain filter to suppress the jamming were proposed. Simulation results show that the recognition rate is better than 90% when the jamming-to-noise ratio is over –3 dB for the received signal. Based on correct recognition, a signal to jamming-and-noise ratio improvement of 18 dB can be achieved using the time-frequency filter.
11
2019, 8(3): 413-424.
For the fast detection of ships in large-scale remote sensing images, a cascade convolutional neural network is proposed, which is a cascade combination of two Fully Convolutional Neural networks (FCNs), the target FCN for Prescreening (P-FCN), and the target FCN for Detection (D-FCN). The P-FCN is a lightweight image classification network that is responsible for the rapid pre-screening of possible ship areas in large-scale images. The region proposals generated by the P-FCN have less redundancy, which can reduce the computational burden of the D-FCN. The D-FCN is an improved U-Net that can accurately detect arbitrary-oriented ships by adding target masks and ship orientation estimation layers to the traditional U-Net structure for multitask learning. In our experiment, TerraSAR-X remote sensing images and the optical remote sensing images obtained from the 91 satellite map software and the DOTA dataset were used to test the network. The results show that the detection accuracy of our method was 0.928 and 0.926 for synthetic aperture radar images and optical images, respectively, which were close to the performance of the traditional sliding window method. However, the running time of the proposed method was only about 1/3 of that of the sliding window method. Therefore, the cascade convolutional neural network can significantly improve the target detection efficiency while maintaining the detection accuracy and can realize the rapid detection of ship targets in large-scale remote sensing images.
For the fast detection of ships in large-scale remote sensing images, a cascade convolutional neural network is proposed, which is a cascade combination of two Fully Convolutional Neural networks (FCNs), the target FCN for Prescreening (P-FCN), and the target FCN for Detection (D-FCN). The P-FCN is a lightweight image classification network that is responsible for the rapid pre-screening of possible ship areas in large-scale images. The region proposals generated by the P-FCN have less redundancy, which can reduce the computational burden of the D-FCN. The D-FCN is an improved U-Net that can accurately detect arbitrary-oriented ships by adding target masks and ship orientation estimation layers to the traditional U-Net structure for multitask learning. In our experiment, TerraSAR-X remote sensing images and the optical remote sensing images obtained from the 91 satellite map software and the DOTA dataset were used to test the network. The results show that the detection accuracy of our method was 0.928 and 0.926 for synthetic aperture radar images and optical images, respectively, which were close to the performance of the traditional sliding window method. However, the running time of the proposed method was only about 1/3 of that of the sliding window method. Therefore, the cascade convolutional neural network can significantly improve the target detection efficiency while maintaining the detection accuracy and can realize the rapid detection of ship targets in large-scale remote sensing images.
12
2019, 8(3): 281-302.
In complex marine environments, sea clutter greatly affects the detection performance of maritime targets. Because the influencing factors of sea clutter are numerous and the mechanism is complex, there are great difficulties in feature description and sea clutter suppression, and it is necessary to carry out long-term, systematic, continuous, and in-depth research. Carrying out sea clutter measurement experiments and obtaining measurement data under the influence of different parameters is an important prerequisite for supporting this research. This paper mainly focuses on the sea clutter measurements that have been carried out. First, typical experiments in various countries such as Canada, South Africa, Australia, the United States, Spain, and Germany are categorized and summarized from the aspects of shore-based experiment and airborne experiment. Then, sea clutter measurement experiments with wave tank conducted by the United States and Japan are reviewed, and domestic sea clutter measurement experiments as well as the construction of the maritime target detection experimental center in Yantai are briefly introduced. Finally, the future research directions that should be emphasized are projected: more systematic and continuous sea clutter measurement experiments need to be conducted; experiment and data analysis under explicit task background need to be strengthened; and sea clutter and target datasets that meet the requirement of intelligent radar applications need to be urgently constructed.
In complex marine environments, sea clutter greatly affects the detection performance of maritime targets. Because the influencing factors of sea clutter are numerous and the mechanism is complex, there are great difficulties in feature description and sea clutter suppression, and it is necessary to carry out long-term, systematic, continuous, and in-depth research. Carrying out sea clutter measurement experiments and obtaining measurement data under the influence of different parameters is an important prerequisite for supporting this research. This paper mainly focuses on the sea clutter measurements that have been carried out. First, typical experiments in various countries such as Canada, South Africa, Australia, the United States, Spain, and Germany are categorized and summarized from the aspects of shore-based experiment and airborne experiment. Then, sea clutter measurement experiments with wave tank conducted by the United States and Japan are reviewed, and domestic sea clutter measurement experiments as well as the construction of the maritime target detection experimental center in Yantai are briefly introduced. Finally, the future research directions that should be emphasized are projected: more systematic and continuous sea clutter measurement experiments need to be conducted; experiment and data analysis under explicit task background need to be strengthened; and sea clutter and target datasets that meet the requirement of intelligent radar applications need to be urgently constructed.
13
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.
14
2019, 8(6): 841-851.
With the development of artificial intelligence, Synthetic-Aperture Radar (SAR) ship detection using deep learning technology can effectively avoid traditionally complex feature design and thereby greatly improve detection accuracy. However, most existing detection models often improve detection accuracy at the expense of detection speed that limits some real-time applications of SAR such as emergency military deployment, rapid maritime rescue, and real-time marine environmental monitoring. To solve this problem, a high-speed and high-accuracy SAR ship detection method called SARShipNet-20 based on a Depthwise Separable Convolution Neural Network (DS-CNN) has been proposed in this paper, that replaces the Traditional Convolution Neural Network (T-CNN) and combines Channel Attention (CA) and Spatial Attention (SA). As a result, high-speed and high-accuracy SAR ship detection can be simultaneously achieved. This method has certain practical significance in the field of real-time SAR application, and its lightweight model is helpful for future FPGA or DSP hardware transplantation.
With the development of artificial intelligence, Synthetic-Aperture Radar (SAR) ship detection using deep learning technology can effectively avoid traditionally complex feature design and thereby greatly improve detection accuracy. However, most existing detection models often improve detection accuracy at the expense of detection speed that limits some real-time applications of SAR such as emergency military deployment, rapid maritime rescue, and real-time marine environmental monitoring. To solve this problem, a high-speed and high-accuracy SAR ship detection method called SARShipNet-20 based on a Depthwise Separable Convolution Neural Network (DS-CNN) has been proposed in this paper, that replaces the Traditional Convolution Neural Network (T-CNN) and combines Channel Attention (CA) and Spatial Attention (SA). As a result, high-speed and high-accuracy SAR ship detection can be simultaneously achieved. This method has certain practical significance in the field of real-time SAR application, and its lightweight model is helpful for future FPGA or DSP hardware transplantation.
15
2019, 8(3): 400-412.
Water segmentation of real SAR images is of great significance in military and civilian applications such as ship target detection and disaster monitoring. To solve the issues of poor robustness and inaccurate segmentation of traditional water segmentation algorithms, this paper first establishes a SAR water segmentation dataset based on the GF3 satellite and then presents a segmentation network architecture based on depthwise separable convolution. The network takes real SAR images as inputs, extracts high-dimensional features through depthwise separable and dilated convolutions, constructs an up-sampling and decoding module based on bilinear interpolation, and then outputs the corresponding segmentation results. The segmentation results of a water segmentation dataset show that the proposed segmentation method remarkably improves the segmentation accuracy, the segmentation robustness and running speed than traditional method. Therefore, the findings demonstrate the excellent practical engineering value of the proposed algorithm.
Water segmentation of real SAR images is of great significance in military and civilian applications such as ship target detection and disaster monitoring. To solve the issues of poor robustness and inaccurate segmentation of traditional water segmentation algorithms, this paper first establishes a SAR water segmentation dataset based on the GF3 satellite and then presents a segmentation network architecture based on depthwise separable convolution. The network takes real SAR images as inputs, extracts high-dimensional features through depthwise separable and dilated convolutions, constructs an up-sampling and decoding module based on bilinear interpolation, and then outputs the corresponding segmentation results. The segmentation results of a water segmentation dataset show that the proposed segmentation method remarkably improves the segmentation accuracy, the segmentation robustness and running speed than traditional method. Therefore, the findings demonstrate the excellent practical engineering value of the proposed algorithm.
16
2019, 8(1): 54-63.
Communication signals are vital to the implementation of integrated radar and communication, which is an effective way to reduce platform volume and electromagnetic interference. In this paper, an integrated radar and communication signal based on multicarrier parameter modulation chirp signal is proposed. Its main carrier adopts the unique chirp signal to implement radar function, while communication information is modulated by the subcarrier with different chirp rates and initial frequency chirp signal. The signal property is analyzed by ambiguity function, and the processing and system performance are studied. Finally, simulation results demonstrate that the proposed sharing signal has a low symbol error rate and high robustness, and communication data transmission can be implemented by slightly degrading the radars performance.
Communication signals are vital to the implementation of integrated radar and communication, which is an effective way to reduce platform volume and electromagnetic interference. In this paper, an integrated radar and communication signal based on multicarrier parameter modulation chirp signal is proposed. Its main carrier adopts the unique chirp signal to implement radar function, while communication information is modulated by the subcarrier with different chirp rates and initial frequency chirp signal. The signal property is analyzed by ambiguity function, and the processing and system performance are studied. Finally, simulation results demonstrate that the proposed sharing signal has a low symbol error rate and high robustness, and communication data transmission can be implemented by slightly degrading the radars performance.
17
2019, 8(6): 717-731.
As an important tool for acquiring remote sensing information, Synthetic Aperture Radar (SAR) has various modes, including high-resolution wide-swath, multi-angle information acquisition, high temporal observation, and three-dimensional topographic mapping. For any spaceborne SAR system, obtaining high-quality images is a prerequisite for improving the performance of SAR applications. In this paper, we analyze the factors affecting spaceborne SAR imaging and image quality with respect to orbit, platform, payload, and signal processing. We describe high-precision data acquisition techniques, including amplitude-phase compensation, the dynamic adjustment of the central electronic equipment, and antenna pattern estimation. We then present imaging compensation methods based on the improved motion model and tropospheric delay correction, which can achieve resolutions better than 0.3 m. Lastly, we summarize and compare SAR image processing techniques such as speckle noise suppression, azimuth ambiguity suppression, and sidelobe suppression, whereby the equivalent number of looks can be increased to more than 25 and the azimuth ambiguity and sidelobes can both be suppressed by 20 dB.
As an important tool for acquiring remote sensing information, Synthetic Aperture Radar (SAR) has various modes, including high-resolution wide-swath, multi-angle information acquisition, high temporal observation, and three-dimensional topographic mapping. For any spaceborne SAR system, obtaining high-quality images is a prerequisite for improving the performance of SAR applications. In this paper, we analyze the factors affecting spaceborne SAR imaging and image quality with respect to orbit, platform, payload, and signal processing. We describe high-precision data acquisition techniques, including amplitude-phase compensation, the dynamic adjustment of the central electronic equipment, and antenna pattern estimation. We then present imaging compensation methods based on the improved motion model and tropospheric delay correction, which can achieve resolutions better than 0.3 m. Lastly, we summarize and compare SAR image processing techniques such as speckle noise suppression, azimuth ambiguity suppression, and sidelobe suppression, whereby the equivalent number of looks can be increased to more than 25 and the azimuth ambiguity and sidelobes can both be suppressed by 20 dB.
18
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.
19
2019, 8(6): 710-716.
The development of multimode high-resolution Synthetic Aperture Radar (SAR) poses new challenges to information perception and feature abstraction of the space, ground, sea, and environment targets. The intersection of spatial remote sensing big data and artificial intelligence information technology is a new scientific research domain and major application area in Automatic Target Recognition (ATR). We emphasize that research on artificial intelligence information technology needs to be conducted under the physical background of the interaction between electromagnetic waves and targets, i.e., physical intelligence, to develop microwave vision of information perception on the electromagnetic spectrum that cannot be recognized by human eyes. This study is based on a keynote speech presented by author at the Fifth Young Scientists Forum of Journal of Radars on August 15, 2019.
The development of multimode high-resolution Synthetic Aperture Radar (SAR) poses new challenges to information perception and feature abstraction of the space, ground, sea, and environment targets. The intersection of spatial remote sensing big data and artificial intelligence information technology is a new scientific research domain and major application area in Automatic Target Recognition (ATR). We emphasize that research on artificial intelligence information technology needs to be conducted under the physical background of the interaction between electromagnetic waves and targets, i.e., physical intelligence, to develop microwave vision of information perception on the electromagnetic spectrum that cannot be recognized by human eyes. This study is based on a keynote speech presented by author at the Fifth Young Scientists Forum of Journal of Radars on August 15, 2019.
20
2019, 8(2): 251-261.
In this paper, the development requirements and challenges of phased array radar design are discussed. A new architecture of phased array radar based on microwave photonic technology is proposed, and its technical advantages are explained. Aiming for applications in engineering practice, the main scientific problems and major technical challenges currently faced are concisely presented from the aspects of their core components, basic transmission links, various processing units, and overall systems. The road map of follow-up research work is given and the future development in this field is finally prospected.
In this paper, the development requirements and challenges of phased array radar design are discussed. A new architecture of phased array radar based on microwave photonic technology is proposed, and its technical advantages are explained. Aiming for applications in engineering practice, the main scientific problems and major technical challenges currently faced are concisely presented from the aspects of their core components, basic transmission links, various processing units, and overall systems. The road map of follow-up research work is given and the future development in this field is finally prospected.
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