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1
2016, 5(3): 320-325.
Abstract
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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.
2
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.
3
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.
4
2016, 5(4): 333-349.
This paper provides a brief review of the development in Unmanned Aerial Vehicle (UAV) borne SAR technology, and gives a summary on the important areas of UAV SAR, including the operation mode, key facilitating technology, performance and specifications, typical systems and applications. According to the characteristics and attributes of UAV platform, the paper focuses on the current development of high resolution, motion compensation and innovative operation mode of the UAV SAR payload. On the demonstration of high resolution, full polarization and interferometric UAV SAR systems, the technologies of top level design on modular reconfiguration, real-time image formation and multi-dimentional motion compensation involved are introduced in detail. Also, the future development trends of UAV SAR technology is discussed as well.
This paper provides a brief review of the development in Unmanned Aerial Vehicle (UAV) borne SAR technology, and gives a summary on the important areas of UAV SAR, including the operation mode, key facilitating technology, performance and specifications, typical systems and applications. According to the characteristics and attributes of UAV platform, the paper focuses on the current development of high resolution, motion compensation and innovative operation mode of the UAV SAR payload. On the demonstration of high resolution, full polarization and interferometric UAV SAR systems, the technologies of top level design on modular reconfiguration, real-time image formation and multi-dimentional motion compensation involved are introduced in detail. Also, the future development trends of UAV SAR technology is discussed as well.
5
2016, 5(2): 143-155.
The study on radar polarization information acquisition and processing has currently been one important part of radar techniques. The development of the polarization theory is simply reviewed firstly. Subsequently, some key techniques which include polarization measurement, polarization anti-jamming, polarization recognition, imaging and parameters inversion using radar polarimetry are emphatically analyzed in this paper. The basic theories, the present states and the development trends of these key techniques are presented and some meaningful conclusions are derived.
The study on radar polarization information acquisition and processing has currently been one important part of radar techniques. The development of the polarization theory is simply reviewed firstly. Subsequently, some key techniques which include polarization measurement, polarization anti-jamming, polarization recognition, imaging and parameters inversion using radar polarimetry are emphatically analyzed in this paper. The basic theories, the present states and the development trends of these key techniques are presented and some meaningful conclusions are derived.
6
Adaptive Wavelet Scale Selection-based Method for Separating Respiration and Heartbeat in Bio-radars
2016, 5(5): 462-469.
Extracting periodic heartbeat signals based on the traditional Fourier transform using a noncontact bio-radar is difficult because chest displacements caused by the heart are much smaller than those caused by respiration. Normally, they can be separated using the continuous wavelet transform; however, the miniscule difference of wavelet scale selection under different conditions may influence the separation performance to some extent. To solve this problem, this study proposes a method based on signal-to-noise ratio calibration to adaptively select the Morletdyadic wavelet scales and then separate the heartbeat signal from the respiration one using the selected scales, which can be applied to detect vital signs of different conditions. The experimental results have exhibited the accuracy and feasibility of the proposed method.
Extracting periodic heartbeat signals based on the traditional Fourier transform using a noncontact bio-radar is difficult because chest displacements caused by the heart are much smaller than those caused by respiration. Normally, they can be separated using the continuous wavelet transform; however, the miniscule difference of wavelet scale selection under different conditions may influence the separation performance to some extent. To solve this problem, this study proposes a method based on signal-to-noise ratio calibration to adaptively select the Morletdyadic wavelet scales and then separate the heartbeat signal from the respiration one using the selected scales, which can be applied to detect vital signs of different conditions. The experimental results have exhibited the accuracy and feasibility of the proposed method.
7
2016, 5(6): 620-638.
As one of the topical research area in the field of radar, polarimetric signal processing techniques gradually receive the attention of scholars worldwide and have been widely applied in various fields.The basis of polarimetric signal processing is to acquire polarization information.In this paper, the research statuses of several relevant key aspects are reviewed, including polarization information acquisition, polarization diversity and coding, polarization anti-interference/clutter, polarization detection, and classification and identification of targets.Finally, the problems faced by radar polarimetry techniques are concluded, and the prospects of future development of the techniques are discussed.
As one of the topical research area in the field of radar, polarimetric signal processing techniques gradually receive the attention of scholars worldwide and have been widely applied in various fields.The basis of polarimetric signal processing is to acquire polarization information.In this paper, the research statuses of several relevant key aspects are reviewed, including polarization information acquisition, polarization diversity and coding, polarization anti-interference/clutter, polarization detection, and classification and identification of targets.Finally, the problems faced by radar polarimetry techniques are concluded, and the prospects of future development of the techniques are discussed.
8
2016, 5(5): 487-498.
An optimal waveform design method that fully employs the knowledge of the target and the environment can further improve target detection performance, thus is of vital importance to research. In this paper, methods of radar waveform optimization for target detection are reviewed and summarized and provide the basis for the research.
An optimal waveform design method that fully employs the knowledge of the target and the environment can further improve target detection performance, thus is of vital importance to research. In this paper, methods of radar waveform optimization for target detection are reviewed and summarized and provide the basis for the research.
9
2016, 5(6): 596-606.
Synthetic Aperture Radar (SAR), an important earth observation sensor, has been used in a wide range of applications for land and marine surveillance.Polarimetric SAR (PolSAR) can obtain abundant scattering information of a target to improve the ability of target detection, classification, and quantitative inversion.In this paper, the important role of PolSAR in ocean monitoring is discussed with factors such as sea ice, ships, oil spill, waves, internal waves, and seabed topography.Moreover, the future development direction of PolSAR is put forward to get an inspiration for further research of PolSAR in marine surveillance applications.
Synthetic Aperture Radar (SAR), an important earth observation sensor, has been used in a wide range of applications for land and marine surveillance.Polarimetric SAR (PolSAR) can obtain abundant scattering information of a target to improve the ability of target detection, classification, and quantitative inversion.In this paper, the important role of PolSAR in ocean monitoring is discussed with factors such as sea ice, ships, oil spill, waves, internal waves, and seabed topography.Moreover, the future development direction of PolSAR is put forward to get an inspiration for further research of PolSAR in marine surveillance applications.
10
2016, 5(6): 692-700.
In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR) images using multiple-feature fusion and ensemble learning.First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images.Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results.Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results.Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.
In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR) images using multiple-feature fusion and ensemble learning.First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images.Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results.Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results.Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.
11
2016, 5(3): 293-301.
Passive radar experiences a significant problem called multipath clutter. The Batch version of the Extensive Cancellation Algorithm (ECA-B) is an efficient method for clutter mitigation. With the increase in signal bandwidth, a greater number of segments is required to cancel the clutter across the entire frequency range. This affects the processing rate, detrimentally weakening and modulating the signal from low-speed targets. Thus, this paper proposes a method that uses ECA-B to process both reference and echo signals in the frequency domain. This method not only reduces the amount of calculation required but also avoids weakening and modulating the target signal, which is spread across many segments. The simulated and experimental data results confirm the correctness and validity of the proposed method.
Passive radar experiences a significant problem called multipath clutter. The Batch version of the Extensive Cancellation Algorithm (ECA-B) is an efficient method for clutter mitigation. With the increase in signal bandwidth, a greater number of segments is required to cancel the clutter across the entire frequency range. This affects the processing rate, detrimentally weakening and modulating the signal from low-speed targets. Thus, this paper proposes a method that uses ECA-B to process both reference and echo signals in the frequency domain. This method not only reduces the amount of calculation required but also avoids weakening and modulating the target signal, which is spread across many segments. The simulated and experimental data results confirm the correctness and validity of the proposed method.
12
2016, 5(3): 229-243.
Forward Scattering Radar (FSR) is a special type of bistatic radar that can implement image detection, imaging, and identification using the forward scattering signals provided by the moving targets that cross the baseline between the transmitter and receiver. Because the forward scattering effect has a vital significance in increasing the targets' Radar Cross Section (RCS), FSR is quite advantageous for use in counter stealth detection. This paper first introduces the front line technology used in forward scattering RCS, FSR detection, and Shadow Inverse Synthetic Aperture Radar (SISAR) imaging and key problems such as the statistical characteristics of forward scattering clutter, accurate parameter estimation, and multitarget discrimination are then analyzed. Subsequently, the current research progress in FSR detection and SISAR imaging are described in detail, including the theories and experiments. In addition, with reference to the BeiDou navigation satellite, the results of forward scattering experiments in civil aircraft detection are shown. Finally, this paper considers future developments in FSR target detection and imaging and presents a new, promising technique for stealth target detection.
Forward Scattering Radar (FSR) is a special type of bistatic radar that can implement image detection, imaging, and identification using the forward scattering signals provided by the moving targets that cross the baseline between the transmitter and receiver. Because the forward scattering effect has a vital significance in increasing the targets' Radar Cross Section (RCS), FSR is quite advantageous for use in counter stealth detection. This paper first introduces the front line technology used in forward scattering RCS, FSR detection, and Shadow Inverse Synthetic Aperture Radar (SISAR) imaging and key problems such as the statistical characteristics of forward scattering clutter, accurate parameter estimation, and multitarget discrimination are then analyzed. Subsequently, the current research progress in FSR detection and SISAR imaging are described in detail, including the theories and experiments. In addition, with reference to the BeiDou navigation satellite, the results of forward scattering experiments in civil aircraft detection are shown. Finally, this paper considers future developments in FSR target detection and imaging and presents a new, promising technique for stealth target detection.
13
2016, 5(6): 647-657.
Knowledge of target polarization characteristics is valuable for radar target detection, classification, and identification.We conducted experimental research on an Unmanned Aerial Vehicle (UAV) with complex materials and structures to determine the differences in polarimetric scattering between the UAV and its perfect electric conductor model.To illustrate the coherence of the entire UAV and its components using polarimetric scattering, we measured and analyzed each part.The results reveal that the airframe and aerofoils directly influence the depolarization, and that the polarimetric scattering characteristics of the airframe represent the primary source for the whole UAV.
Knowledge of target polarization characteristics is valuable for radar target detection, classification, and identification.We conducted experimental research on an Unmanned Aerial Vehicle (UAV) with complex materials and structures to determine the differences in polarimetric scattering between the UAV and its perfect electric conductor model.To illustrate the coherence of the entire UAV and its components using polarimetric scattering, we measured and analyzed each part.The results reveal that the airframe and aerofoils directly influence the depolarization, and that the polarimetric scattering characteristics of the airframe represent the primary source for the whole UAV.
14
2016, 5(3): 284-292.
While Wireless Fidelity (WiFi)-based passive radar can achieve high detection resolution in both the range and Doppler domain, it is difficult to extract the reference signal because of the complexities of its signal format and application scenarios. In this study, we analyze a typical application of WiFi-based passive radar and discuss different methods for reference signal extraction. Based on the format and features of WiFi signals, we propose a method for reference signal reconstruction, and analyze the influence of the reconstructed reference signal's performance on detection. The results show that higher reference SNRs generate lower decoding bit rate errors and better clutter suppression with the reconstructed reference signal. Moreover, we propose a method for removing irrelevant signals to avoid the impact on target detection of a non-direct path signal in the receiving signal. The experimental results validate the efficacy of the proposed signal processing method.
While Wireless Fidelity (WiFi)-based passive radar can achieve high detection resolution in both the range and Doppler domain, it is difficult to extract the reference signal because of the complexities of its signal format and application scenarios. In this study, we analyze a typical application of WiFi-based passive radar and discuss different methods for reference signal extraction. Based on the format and features of WiFi signals, we propose a method for reference signal reconstruction, and analyze the influence of the reconstructed reference signal's performance on detection. The results show that higher reference SNRs generate lower decoding bit rate errors and better clutter suppression with the reconstructed reference signal. Moreover, we propose a method for removing irrelevant signals to avoid the impact on target detection of a non-direct path signal in the receiving signal. The experimental results validate the efficacy of the proposed signal processing method.
15
2016, 5(4): 361-372.
Orthogonal Frequency-Division Multiplexing (OFDM) radar is receiving increasing attention in the radar field in recent years and is showing excellent performance. However, for practical applications, there are several problems with phase-coded OFDM radar, such as the existence of few good codes, limited length capability, and a high Peak-to-Mean-Envelope Power Ratio (PMEPR). To address those problems, in this paper, we propose a design method for a four-phase-coded OFDM radar signal based on Bernoulli chaos, which can construct codes of arbitrary amounts and lengths and demonstrate more agility and flexibility. By adopting original phase weighting, this method can obtain a chaotic four-phase-coded OFDM signal with a PMEPR less than two. This signal has excellent performance with respect to high resolution and Doppler radar application.
Orthogonal Frequency-Division Multiplexing (OFDM) radar is receiving increasing attention in the radar field in recent years and is showing excellent performance. However, for practical applications, there are several problems with phase-coded OFDM radar, such as the existence of few good codes, limited length capability, and a high Peak-to-Mean-Envelope Power Ratio (PMEPR). To address those problems, in this paper, we propose a design method for a four-phase-coded OFDM radar signal based on Bernoulli chaos, which can construct codes of arbitrary amounts and lengths and demonstrate more agility and flexibility. By adopting original phase weighting, this method can obtain a chaotic four-phase-coded OFDM signal with a PMEPR less than two. This signal has excellent performance with respect to high resolution and Doppler radar application.
16
Sparse signal processing has been utilized to the area of radar sensing. Due to the presence of unknown factors such as the motion of the targets of interest and the error of the radar trajectory, a predesigned dictionary cannot provide the optimally spare representation of the actual radar signals. This paper will introduce a method called parametric sparse representation, which is a special case of dictionary learning and can dynamically learn the unknown factors during the radar sensing and achieve the optimally sparse representation of radar signals. This paper will also introduce the applications of parametric sparse representation to Inverse Synthetic Aperture Radar imaging (ISAR) imaging, Synthetic Aperture Radar imaging (SAR) autofocusing and target recognition based on micro-Doppler effect.
Sparse signal processing has been utilized to the area of radar sensing. Due to the presence of unknown factors such as the motion of the targets of interest and the error of the radar trajectory, a predesigned dictionary cannot provide the optimally spare representation of the actual radar signals. This paper will introduce a method called parametric sparse representation, which is a special case of dictionary learning and can dynamically learn the unknown factors during the radar sensing and achieve the optimally sparse representation of radar signals. This paper will also introduce the applications of parametric sparse representation to Inverse Synthetic Aperture Radar imaging (ISAR) imaging, Synthetic Aperture Radar imaging (SAR) autofocusing and target recognition based on micro-Doppler effect.
17
2016, 5(6): 559-595.
The application performance of Synthetic Aperture Radar (SAR) instruments is generally limited in their capability to acquire radar images with both high-resolution and wide swath coverage.The available swath width of Polarimetric SAR (PolSAR) systems is even more restricted.Recently, a new PolSAR architecture called the Hybrid-Polarity (HP) architecture has attracted worldwide attentions.Compared with conventional linearly-polarized PolSARs, HP architecture based PolSARs have significant advantages such as wider swath coverage and lower hardware requirement.In this paper, the principles of the HP architecture, including system designs, system models and calibration methods are first reviewed.Two implementation difficulties of the HP architecture, concerning calibration issue and transmit configuration are illustrated.In order to overcome these problems, an improved version of the HP architecture is proposed.A prototype system based on this improved HP architecture developed for experimental validation is also introduced.In the latter part of this paper, applications suitable for the HP architecture based PolSARs are reviewed.Since the quadrature-polarimetric (quad-pol) data provided by an HP architecture based PolSAR system may be directly transformed into conventional linearly-polarized quad-pol data, this part of review is mainly focused on the corresponding dual-pol applications, i.e.Compact Polarimetry (CP) applications.
The application performance of Synthetic Aperture Radar (SAR) instruments is generally limited in their capability to acquire radar images with both high-resolution and wide swath coverage.The available swath width of Polarimetric SAR (PolSAR) systems is even more restricted.Recently, a new PolSAR architecture called the Hybrid-Polarity (HP) architecture has attracted worldwide attentions.Compared with conventional linearly-polarized PolSARs, HP architecture based PolSARs have significant advantages such as wider swath coverage and lower hardware requirement.In this paper, the principles of the HP architecture, including system designs, system models and calibration methods are first reviewed.Two implementation difficulties of the HP architecture, concerning calibration issue and transmit configuration are illustrated.In order to overcome these problems, an improved version of the HP architecture is proposed.A prototype system based on this improved HP architecture developed for experimental validation is also introduced.In the latter part of this paper, applications suitable for the HP architecture based PolSARs are reviewed.Since the quadrature-polarimetric (quad-pol) data provided by an HP architecture based PolSAR system may be directly transformed into conventional linearly-polarized quad-pol data, this part of review is mainly focused on the corresponding dual-pol applications, i.e.Compact Polarimetry (CP) applications.
18
2016, 5(4): 350-360.
Vacuum Electronic Devices (VEDs) which are considered as the heart of a radar system, play an important role in their development. VEDs and radar systems supplement and promote each other. Some new trends in VEDs have been observed with advancements in the simulation tools for designing VEDs, new materials, new fabrication techniques. Recently, the performance of VEDs has greatly improved. In addition, new devices have been invented, which have laid the foundation for the developments of radar detection technology. This study introduces the recent development trends and research results of VEDs from microwave and millimeter wave devices and power modules, integrated VEDs, terahertz VEDs, and high power VEDs.
Vacuum Electronic Devices (VEDs) which are considered as the heart of a radar system, play an important role in their development. VEDs and radar systems supplement and promote each other. Some new trends in VEDs have been observed with advancements in the simulation tools for designing VEDs, new materials, new fabrication techniques. Recently, the performance of VEDs has greatly improved. In addition, new devices have been invented, which have laid the foundation for the developments of radar detection technology. This study introduces the recent development trends and research results of VEDs from microwave and millimeter wave devices and power modules, integrated VEDs, terahertz VEDs, and high power VEDs.
19
2016, 5(4): 444-452.
For real-time autofocus of defocused images produced by Synthetic Aperture Radar (SAR), the twodimensional autofocus approach proposed in this study is used to correct the residual range cell migration and compensate for the phase error. Next, a block-wise Phase Gradient Autofocus (PGA) is used to correct the space-variant phase error. The Field-Programmable Gate Array (FPGA) design procedures, resource utilization, processing speed, accuracy, and autofocus are discussed in detail. The system is able to autofocus an 8K 8K complex image with single precision within 5.7 s when the FPGA works at 200 MHz. The processing of the measured data verifies the effectiveness and real-time capability of the proposed method.
For real-time autofocus of defocused images produced by Synthetic Aperture Radar (SAR), the twodimensional autofocus approach proposed in this study is used to correct the residual range cell migration and compensate for the phase error. Next, a block-wise Phase Gradient Autofocus (PGA) is used to correct the space-variant phase error. The Field-Programmable Gate Array (FPGA) design procedures, resource utilization, processing speed, accuracy, and autofocus are discussed in detail. The system is able to autofocus an 8K 8K complex image with single precision within 5.7 s when the FPGA works at 200 MHz. The processing of the measured data verifies the effectiveness and real-time capability of the proposed method.
20
2016, 5(1): 57-64.
While the use of SAR Tomography (TomoSAR) based on Compressive Sensing (CS) makes it possible to reconstruct the height profile of an observed scene, the performance of the reconstruction decreases for a structural observed scene. To deal with this issue, we propose using TomoSAR based on Block Compressive Sensing (BCS), which changes the reconstruction of the structural observed scene into a BCS problem under the principles of CS. Further, the block size is established by utilizing the relationship between the characteristics of the structural observed scene and the SAR parameters, such that the BCS problem is efficiently solved with a block sparse l1/l2 norm optimization signal model. Compared with existing CSTomoSAR methods, the proposed BCS-TomoSAR method makes better use of the sparsity and structure information of a structural observed scene, and has higher precision and better reconstruction performance. We used simulations and Radarsat-2 data to verify the effectiveness of this proposed method.
While the use of SAR Tomography (TomoSAR) based on Compressive Sensing (CS) makes it possible to reconstruct the height profile of an observed scene, the performance of the reconstruction decreases for a structural observed scene. To deal with this issue, we propose using TomoSAR based on Block Compressive Sensing (BCS), which changes the reconstruction of the structural observed scene into a BCS problem under the principles of CS. Further, the block size is established by utilizing the relationship between the characteristics of the structural observed scene and the SAR parameters, such that the BCS problem is efficiently solved with a block sparse l1/l2 norm optimization signal model. Compared with existing CSTomoSAR methods, the proposed BCS-TomoSAR method makes better use of the sparsity and structure information of a structural observed scene, and has higher precision and better reconstruction performance. We used simulations and Radarsat-2 data to verify the effectiveness of this proposed method.
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