基于属性散射信息的随机梯度最小方差追踪SAR超分辨重建算法
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摘要
无论军事还是民用合成孔径雷达(SAR)应用领域,对实现目标更高分辨、更精细描述的期望和需求都十分迫切。在稀疏表示框架下,构建了基于属性散射中心模型(ASC)部件级局部散射模型的SAR重建观测模型;提出一种基于信号域的散射中心属性参数空间分类策略,并联合频域外推,提出一种基于随机梯度最小方差追踪的部件级超分辨SAR重建算法。该算法最终的超分辨SAR图像由FFT获得,提高了算法效率;并且该算法实现了在重建超分辨SAR图像的同时获取高精度的目标散射中心属性级特征。仿真合成数据和电磁计算数据验证了算法的超分辨能力,并利用ASC属性的克拉美罗界对算法属性估计性能进行了评估。
The applications in both the military and civil field have pressing needs and great expectations of achieving the target higher resolution and more detailed description. This paper firstly modeled the object-level SAR observations based on attributed scattering center( ASC) model in sparse representation framework. Secondly,it proposed a classifying strategy of the target attributes space for the object-level reconstruction in signal domain. Combined with data extrapolating,then it proposed a stochastic gradient minimum variance pursuit( SGMVP) based object-level super-resolution reconstruction algorithm. It finally achieved super-resolution image by FFT to effectively promotethe efficiency of the proposed algorithm. The proposed algorithm not only can achieve improved super-resolution image,but also provide accurate physically-relevant attributed features of the scatterers simultaneously. Experimental results confirm the effectiveness of the proposed algorithm.
The applications in both the military and civil field have pressing needs and great expectations of achieving the target higher resolution and more detailed description. This paper firstly modeled the object-level SAR observations based on attributed scattering center( ASC) model in sparse representation framework. Secondly,it proposed a classifying strategy of the target attributes space for the object-level reconstruction in signal domain. Combined with data extrapolating,then it proposed a stochastic gradient minimum variance pursuit( SGMVP) based object-level super-resolution reconstruction algorithm. It finally achieved super-resolution image by FFT to effectively promotethe efficiency of the proposed algorithm. The proposed algorithm not only can achieve improved super-resolution image,but also provide accurate physically-relevant attributed features of the scatterers simultaneously. Experimental results confirm the effectiveness of the proposed algorithm.
引文
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[16]向高,张晓玲,师君,等.基于随机阵列的降维压缩感知三维成像方法[J].计算机应用研究,2015,33(1):286-290.(Xiang Gao,Zhang Xiaoling,Shi Jun,et al.Compressed sensing 3D imaging method with dimension reduction for random array[J].Applications Research of Computers,2015,33(1):286-290.
[17]Akyildiz Y.Feature extraction from synthetic aperture radar imagery[D].Columbus:The Ohio State University,2000:17-55.
[2]Flake L R,Ahalt S C,Krishnamurthy A K.Detecting anisotropic scattering with hidden Markov models[J].IEE Proceedings-Radar,Sonar and Navigation,1997,144(2):81-86.
[3]Cetin M,Karl W C.Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization[J].IEEE Trans on Image Processing,2001,10(4):623-631.
[4]Cong Xunchao,Gui Guan,Li Xiao,et al.Object-level SAR imaging method with canonical scattering characterisation and inter-subdictionary interferences mitigation[J].IET Radar,Sonar&Navigation,2016,10(4):784-790.
[5]Varshney K R,Cetin M,Fisher J W,et al.Sparse representation in structured dictionaries with application to synthetic aperture radar[J].IEEE Trans on Signal Processing,2008,56(8):3548-3561.
[6]Jackson J A,Moses R L.Synthetic aperture radar 3D feature extraction for arbitrary flight paths[J].IEEE Trans on Aerospace and Electronic Systems,2012,48(3):2065-2084.
[7]Liao Kefei,Zhang Xiaoling,Shi Jun.Plane-wave synthesis and RCSextraction via 3-D linear array SAR[J].IEEE Antennas and Wireless Propagation Letters,2015,14(1):994-997.
[8]Guo Kunyi,Qu Quanyou,Sheng Xinqing.Geometry reconstruction based on attributes of scattering centers by using time-frequency representations[J].IEEE Trans on Antennas and Propagation,2016,64(2):708-720.
[9]Keller J B.Geometrical theory of diffraction[J].Journal of the Optical Society of America,1962,52(2):116-130.
[10]Hammond G B,Jackson J A.SAR canonical feature extraction using molecule dictionaries[C]//Proc of Radar Conference.Piscataway,NJ:IEEE Press,2013:1-6.
[11]Potter L C,Moses R L.Attributed scattering centers for SAR ATR[J].IEEE Trans on Image Processing,1997,6(1):79-91.
[12]Candès E J,Tao T.Decoding by linear programming[J].IEEE Trans on Information Theory,2005,51(12):4203-4215.
[13]Lin Yumin,Chen Yi,Huang Naishan,et al.Low-complexity stochastic gradient pursuit algorithm and architecture for robust compressive sensing reconstruction[J].IEEE Trans on Signal Processing,2016,65(3):638-650.
[14]Liu Hongchao,Jiu Bo,Liu Hongwei,et al.Superresolution ISAR imaging based on sparse Bayesian learning[J].IEEE Trans on Geoscience and Remote Sensing,2014,52(8):5005-5013.
[15]Zhang Lei,Qiao Zhijun,Xing Mengdao,et al.High-resolution ISARimaging with sparse stepped-frequency waveforms[J].IEEE Trans on Geoscience and Remote Sensing,2011,49(11):4630-4651.
[16]向高,张晓玲,师君,等.基于随机阵列的降维压缩感知三维成像方法[J].计算机应用研究,2015,33(1):286-290.(Xiang Gao,Zhang Xiaoling,Shi Jun,et al.Compressed sensing 3D imaging method with dimension reduction for random array[J].Applications Research of Computers,2015,33(1):286-290.
[17]Akyildiz Y.Feature extraction from synthetic aperture radar imagery[D].Columbus:The Ohio State University,2000:17-55.