SAR微动目标的稀疏贝叶斯成像方法

SAR微动信息能够反映出目标的属性信息,其微动图像可作为雷达目标识别的一种重要手段。基于SAR微动目标回波的稀疏特性,建立了在过完备词典下的稀疏表示模型,提出一种新的稀疏贝叶斯重构方法——方差成分扩张压缩,该方法仅赋予有重要意义的信号元素不同的方差分量,拥有更少的参数。仿真结果表明,方差成分扩张压缩方法能较精确地估计出SAR目标微动参数,同时能够获得低信噪比条件下较好的微动目标像。

The SAR micro motion target imaging via the sparse Bayesian method

Micro motion target image can be used in radar target recognition since they can reflect the attribute of target information. Based on the fact that the echo of the SAR micro motion target is sparse, the sparse signal representation is established under an over-complete dictionary in this paper. By taking into account of the prior information of the sparse signal and the additive noise encountered in the measurement process, the sparse Bayesian recover algorithm can reconstruct the coefficient better than the conventional recover algorithm. However, the traditional Sparse Bayesian Learning (SBL) hold many parameters and its timeliness is poor. In this paper, a new sparse Bayesian learning named expansion-compression variance-component based method (ExCoV) is considered, which only endows a different variance-component to the significant signal elements. Unlikely, the SBL has a distinct variance component on the all signal elements. In addition, the ExCoV have much less parameters than the SBL. Compared the traditional micro motion imaging methods, the imaging results of SAR micro motion target can estimate the micro motion parameter better, and achieve the better SAR micro motion image under the low SNR.