An Autofocus Imaging Method for ISAR Based on Bayesian Compressive Sensing
Wang Tian-yun①② Lu Xin-fei① Sun Lin① Chen Chang① Chen Wei-dong①
①(Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China, Hefei 230027, China) ②(China Satellite Maritime Tracking and Control Department, Jiangyin 214431, China)
For Inverse Synthetic Aperture Radar (ISAR) autofocus imaging, this paper proposes a high-resolution imaging method based on Bayesian Compressed Sensing (BCS). Firstly, according to the sparsity characteristics of target image, a sparse model with the hierarchical framework is established, which can achieve better approximation to the original l0 norm. Then, the phase errors are assumed to obey the uniform distribution. Next, following the criterion of Maximum A Posteriori (MAP), target image and phase errors are solved using alternate iteration based on BCS theory. Compared with traditional methods, the proposed method further combines the joint sparse information of target image, and converts the ISAR CS imaging into solving a joint Multiple Measurement Vector (MMV) sparse optimization problem, which can improve both the autofocus precision and the imaging quality efficiently. Simulation results show the effectiveness of the proposed method.
王天云,陆新飞,孙麟,陈畅,陈卫东. 基于贝叶斯压缩感知的ISAR自聚焦成像[J]. 电子与信息学报, 2015, 37(11): 2719-2726.
Wang Tian-yun, Lu Xin-fei, Sun Lin, Chen Chang, Chen Wei-dong. An Autofocus Imaging Method for ISAR Based on Bayesian Compressive Sensing. JEIT, 2015, 37(11): 2719-2726.
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