一种利用稀疏统计特性的超分辨ISAR成像方法

Super-resolution ISAR imaging method with sparse statistics

为提高短孔径逆合成孔径雷达的成像分辨率,利用逆合成孔径雷达图像的稀疏统计特性,提出了一种超分辨成像算法.通过结合逆合成孔径雷达像的强稀疏性,对成像过程建立近似的统计概率分布模型.利用最大后验概率及贝叶斯估计方法,推导了稀疏控制参数的显式表达,并通过共轭梯度法优化求解图像.另外,联合恒虚警概率检测和带宽外推技术的步进式成像过程,提高了参数估计和超分辨成像算法的稳健性.

To improve the resolution of inverse synthetic aperture radar（ISAR） imagery with short observation,this paper proposes a super-resolution method for ISAR imaging with sparse statistics.Combined with strong sparsity of ISAR images,the imaging process is approximately modeled by statistical probability distributions.By maximum posterior probability and the Bayesian estimation approaches,the close form of sparsity control parameters is deduced,and the optimal image is solved by the conjugate gradient method.Also,with the CFAR（constant false alarm rate） and bandwidth extrapolation technique involved in a stage-by-stage procedure,the robustness of parameter estimation and imaging is improved.Real-data experiments validate the superiority of the proposed method.