Autofocus technique for ISAR imaging of uniformly rotating targets based on the ExCoV method  

作　　者：Chengguang Wu Hongqiang Wang Bin Deng Yuliang Qin Wuge Su 

机构地区：[1]Institute of Space Electronic Technology, School of Electronic Science and Engineering,National University of Defense Technology, Changsha 410073, China

出　　处：《Journal of Systems Engineering and Electronics》2017年第2期267-275,共9页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation(61302148)

摘　　要：The inverse synthetic aperture radar (ISAR) imaging can be converted into a sparse reconstruction problem and solved by the l1norm minimization algorithm. The basis matrix in sparse ISAR imaging is usually characterized by the unknown rotation rate of a moving target, thus the rotation rate and the sparse signal should be jointly estimated. Especially due to the imperfect coarse motion compensation, we consider the phase error correction problem in the context of the sparse signal reconstruction. To address this issue, we propose an iterative reweighted method, which jointly estimates the rotation rate, corrects the phase error and reconstructs a high resolution ISAR image. The proposed method gives a gradual and interweaved iterative process to refine the unknown parameters to achieve the best sparse representation for the ISAR signals. Particularly, in ISAR image reconstruction, the l1norm minimization algorithm is sensitive to user parameters. Setting these user parameters are not trivial and the reconstruction performance depends significantly on their choices. Then, we consider an expansion-compression variance-component (ExCoV) based method, which is automatic and demands no prior knowledge about signal-sparsity or measurement-noise levels. Both numerical and electromagnetic data experiments are implemented to show the effectiveness of the proposed method. It is shown that the proposed method can estimate the rotation rate and correct the phase errors simultaneously, and its superior performance is proved in terms of high resolution ISAR image. © 2017 Beijing Institute of Aerospace Information.The inverse synthetic aperture radar (ISAR) imaging can be converted into a sparse reconstruction problem and solved by the l1norm minimization algorithm. The basis matrix in sparse ISAR imaging is usually characterized by the unknown rotation rate of a moving target, thus the rotation rate and the sparse signal should be jointly estimated. Especially due to the imperfect coarse motion compensation, we consider the phase error correction problem in the context of the sparse signal reconstruction. To address this issue, we propose an iterative reweighted method, which jointly estimates the rotation rate, corrects the phase error and reconstructs a high resolution ISAR image. The proposed method gives a gradual and interweaved iterative process to refine the unknown parameters to achieve the best sparse representation for the ISAR signals. Particularly, in ISAR image reconstruction, the l1norm minimization algorithm is sensitive to user parameters. Setting these user parameters are not trivial and the reconstruction performance depends significantly on their choices. Then, we consider an expansion-compression variance-component (ExCoV) based method, which is automatic and demands no prior knowledge about signal-sparsity or measurement-noise levels. Both numerical and electromagnetic data experiments are implemented to show the effectiveness of the proposed method. It is shown that the proposed method can estimate the rotation rate and correct the phase errors simultaneously, and its superior performance is proved in terms of high resolution ISAR image. © 2017 Beijing Institute of Aerospace Information.

关 键 词：Error compensation Error correction Errors Image processing Image reconstruction Inverse problems Inverse synthetic aperture radar Iterative methods Motion compensation Numerical methods Rotation Signal reconstruction Synthetic aperture radar 

分 类 号：TN957.52[电子电信—信号与信息处理]