摘要 :
Three-dimensional (3-D) radar imaging is an important task underpinning many applications that seek target detection, localization, and classification. However, traditional 3-D radar imaging is computationally cumbersome and needs...
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Three-dimensional (3-D) radar imaging is an important task underpinning many applications that seek target detection, localization, and classification. However, traditional 3-D radar imaging is computationally cumbersome and needs a large memory space to store the cubic data matrix. These challenges have hindered 3-D radar imaging development and limited its applications. Owing to the sparsity of the radar image of typical targets, the data size could be significantly reduced by exploiting compressive sensing and sparse reconstruction techniques. These techniques prove important to mitigate the sidelobe levels that arise from successive Fourier-based processing. Towards this end, we first perform polar reformatting to the 2-D data matrix in azimuth and range. Then, a series of images over different values of the vertical variable z are generated by using different focusing filters. Sparse optimization is afterwards applied to the 3-D data cube to produce high-resolution, significantly reduced sidelobe 3-D image. Compared with the conventional 3-D radar imaging methods, the proposed method, in addition to high fidelity images, requires fewer data measurements and offers computationally efficient processing. Numerical simulations are provided to evaluate the effectiveness of the proposed method.
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摘要 :
Three-dimensional (3-D) radar imaging is an important task underpinning many applications that seek target detection, localization, and classification. However, traditional 3-D radar imaging is computationally cumbersome and needs...
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Three-dimensional (3-D) radar imaging is an important task underpinning many applications that seek target detection, localization, and classification. However, traditional 3-D radar imaging is computationally cumbersome and needs a large memory space to store the cubic data matrix. These challenges have hindered 3-D radar imaging development and limited its applications. Owing to the sparsity of the radar image of typical targets, the data size could be significantly reduced by exploiting compressive sensing and sparse reconstruction techniques. These techniques prove important to mitigate the sidelobe levels that arise from successive Fourier-based processing. Towards this end, we first perform polar reformatting to the 2-D data matrix in azimuth and range. Then, a series of images over different values of the vertical variable z are generated by using different focusing filters. Sparse optimization is afterwards applied to the 3-D data cube to produce high-resolution, significantly reduced sidelobe 3-D image. Compared with the conventional 3-D radar imaging methods, the proposed method, in addition to high fidelity images, requires fewer data measurements and offers computationally efficient processing. Numerical simulations are provided to evaluate the effectiveness of the proposed method.
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Since the United States launched the first synthetic aperture radar satellite in 1978, it has attracted widespread attention because of its ability to obtain high-resolution images of almost every corner of the earth, all day, all...
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Since the United States launched the first synthetic aperture radar satellite in 1978, it has attracted widespread attention because of its ability to obtain high-resolution images of almost every corner of the earth, all day, all weather, and regardless of national borders and politics. At present, spaceborne SAR has been widely used in civil and military applications. With the complexity of spaceborne SAR application scenarios, different imaging widths and imaging resolutions are required to deal with different imaging scenarios, imaging targets, and imaging tasks. For different imaging requirements, different imaging algorithms have been proposed, including strip imaging mode, spotlight imaging mode, scan imaging mode, etc. It is of great significance to integrate multiple imaging modes in a spaceborne SAR. Different imaging modes have different requirements for antenna beam pointing. And each imaging mode has the need to quickly switch beam pointing. The phased array antenna can realize the switching of beam pointing with different beam control codes, and the speed of beam direction switching depends on the calculation speed of beam control code. Therefore, the calculation strategy and the calculation speed of the beam control code have become the main problems which restricting the multi-mode imaging of spaceborne SAR. In this paper, a real-time antenna beam control method based on the computing framework of DSP, FPGA and CPU is proposed. According to different imaging modes, the corresponding beam control code calculation strategy is designed. The test results show that relying on the high-performance computing power of DSP and the logic control capabilities of FPGA and CPU, it perfectly meets the needs of quickly completing beam control code calculation to realize beam pointing switching in different imaging modes. This method makes it possible to realize multimode imaging of spaceborne SAR.
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Inverse synthetic aperture radar (ISAR) imaging has been widely developed and applied in many applications, such as, radar target recognition, autopilot and seaport ship surveillance, etc. However, low signal-to-noise ratio (SNR) ...
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Inverse synthetic aperture radar (ISAR) imaging has been widely developed and applied in many applications, such as, radar target recognition, autopilot and seaport ship surveillance, etc. However, low signal-to-noise ratio (SNR) environments may impede conventional range profiles-based translational motion compensation (TMC), causing image degradation. In this paper, a novel method combining profiles and phase is proposed which utilizes two-dimensional (2-D) coherent accumulation to overcome the low SNR problem, even when the range profiles are submerged into noise. The method only requires Fast Fourier transform (FFT) and Hadamard multiplication operations which renders it suitable for real-time applications. Simulation results are provided to validate the performance of the proposed TMC method compared with the state-of-art TMC methods.
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摘要 :
Inverse synthetic aperture radar (ISAR) imaging has been widely developed and applied in many applications, such as, radar target recognition, autopilot and seaport ship surveillance, etc. However, low signal-to-noise ratio (SNR) ...
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Inverse synthetic aperture radar (ISAR) imaging has been widely developed and applied in many applications, such as, radar target recognition, autopilot and seaport ship surveillance, etc. However, low signal-to-noise ratio (SNR) environments may impede conventional range profiles-based translational motion compensation (TMC), causing image degradation. In this paper, a novel method combining profiles and phase is proposed which utilizes two-dimensional (2-D) coherent accumulation to overcome the low SNR problem, even when the range profiles are submerged into noise. The method only requires Fast Fourier transform (FFT) and Hadamard multiplication operations which renders it suitable for real-time applications. Simulation results are provided to validate the performance of the proposed TMC method compared with the state-of-art TMC methods.
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Stepped frequency chirps can be used to generate high resolution range profiles (HRRP) by using spectrum synthesis. There are three distributed modes of stepped frequency SAR (SF-SAR). However, the presence of channel phase errors...
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Stepped frequency chirps can be used to generate high resolution range profiles (HRRP) by using spectrum synthesis. There are three distributed modes of stepped frequency SAR (SF-SAR). However, the presence of channel phase errors may degrade the performance of HRRP synthesis. This letter presents a unified error estimation method to address this problem. First, to obtain a focused sub-band image, a range phase adjustment by contrast enhancement (RPACE) algorithm is proposed to estimate inner-channel high-order phase errors. Second, a side-lobe balanced model (SLBM) is established to estimate constant phase error from the relationship between the balanced side-lobe and constant phase; the constant phase error can be directly obtained in an efficient manner. Experimental analysis using real data demonstrates the effectiveness of the proposed method.
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摘要 :
Stepped frequency chirps can be used to generate high resolution range profiles (HRRP) by using spectrum synthesis. There are three distributed modes of stepped frequency SAR (SF-SAR). However, the presence of channel phase errors...
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Stepped frequency chirps can be used to generate high resolution range profiles (HRRP) by using spectrum synthesis. There are three distributed modes of stepped frequency SAR (SF-SAR). However, the presence of channel phase errors may degrade the performance of HRRP synthesis. This letter presents a unified error estimation method to address this problem. First, to obtain a focused sub-band image, a range phase adjustment by contrast enhancement (RPACE) algorithm is proposed to estimate inner-channel high-order phase errors. Second, a side-lobe balanced model (SLBM) is established to estimate constant phase error from the relationship between the balanced side-lobe and constant phase; the constant phase error can be directly obtained in an efficient manner. Experimental analysis using real data demonstrates the effectiveness of the proposed method.
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GEOSAR has the characteristics of wide coverage and short revisit time. But when the GEOSAR is both used as a transmitter and a receiver, its advantages is not well exploited. If an airplane or a LEO satellite is replaced as a pla...
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GEOSAR has the characteristics of wide coverage and short revisit time. But when the GEOSAR is both used as a transmitter and a receiver, its advantages is not well exploited. If an airplane or a LEO satellite is replaced as a platform of the receiver, we can not only observe the interesting regions flexibly, but also achieve finer resolution. However, the geometry of the BiSAR is complicated. Thus, it's not easy to acquire the resolution characteristics of an arbitrary BiSAR system. In this paper, starting with the resolution on the basic plane of a BiSAR system, and combining the resolution's projection relation between the basic plane and the plane tangent to the earth's surface, we obtain the resolution ellipse's expression on the ground. Finally, based on the expression, we optimize the two parameters including the signal bandwidth and the synthetic aperture time in order to realize the satisfactory resolution in a BiSAR system.
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摘要 :
GEOSAR has the characteristics of wide coverage and short revisit time. But when the GEOSAR is both used as a transmitter and a receiver, its advantages is not well exploited. If an airplane or a LEO satellite is replaced as a pla...
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GEOSAR has the characteristics of wide coverage and short revisit time. But when the GEOSAR is both used as a transmitter and a receiver, its advantages is not well exploited. If an airplane or a LEO satellite is replaced as a platform of the receiver, we can not only observe the interesting regions flexibly, but also achieve finer resolution. However, the geometry of the BiSAR is complicated. Thus, it's not easy to acquire the resolution characteristics of an arbitrary BiSAR system. In this paper, starting with the resolution on the basic plane of a BiSAR system, and combining the resolution's projection relation between the basic plane and the plane tangent to the earth's surface, we obtain the resolution ellipse's expression on the ground. Finally, based on the expression, we optimize the two parameters including the signal bandwidth and the synthetic aperture time in order to realize the satisfactory resolution in a BiSAR system.
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Synthetic aperture radar (SAR) raw data simulator is an important tool for parameter-optimizing and algorithm-testing, particularly for those complicated configurations in which real raw data is difficult to obtain. As a new and s...
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Synthetic aperture radar (SAR) raw data simulator is an important tool for parameter-optimizing and algorithm-testing, particularly for those complicated configurations in which real raw data is difficult to obtain. As a new and special imaging mode, bistatic forward-looking SAR with constant acceleration (BFCA-SAR) can perform two-dimensional imaging for targets in the straight-ahead position over mono-static SAR. But there exist more complicated square roots and high-order terms in range history owing to high velocities and accelerations from both platforms. In addition, space variances in phase terms of two-dimensional frequency spectrum (2-D FS) make it difficult to gain echo data accurately. In this paper, a fast scene raw data simulator for BFCA-SAR based on quantitative analysis and effective correction of phase space variance is proposed. With high precision, our method can generate raw data more efficiently than traditional algorithms.
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