CAJ | 학술논문

针对ISAR自聚焦成像，该文提出一种基于贝叶斯压缩感知的高分辨率成像算法。首先利用目标图像的稀疏特性构建级联形式的稀疏先验模型，同时将相位误差建模为均匀分布模型；然后基于最大后验准则，依据贝叶斯压缩感知理论交替迭代求解目标图像和相位误差。与传统稀疏方法相比，所提算法进一步利用了目标图像的联合稀疏信息，将ISAR CS成像转化为MMV联合稀疏优化问题的求解，可以有效改善自聚焦的精度以及成像质量。仿真结果验证了该算法的有效性。
침대ISAR자취초성상，해문제출일충기우패협사압축감지적고분변솔성상산법。수선이용목표도상적희소특성구건급련형식적희소선험모형，동시장상위오차건모위균균분포모형；연후기우최대후험준칙，의거패협사압축감지이론교체질대구해목표도상화상위오차。여전통희소방법상비，소제산법진일보이용료목표도상적연합희소신식，장ISAR CS성상전화위MMV연합희소우화문제적구해，가이유효개선자취초적정도이급성상질량。방진결과험증료해산법적유효성。
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 originall0norm. Then, the phase errors are assumed to obey the uniform distribution. Next, following thecriterion 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.