基于扩展目标先验的贝叶斯压缩感知成像

Bayesian Compressed Sensing Imaging for Extended Target Based on Distribution of Prior Information

已有的基于压缩感知理论的雷达成像技术通常是基于待重构目标散射点自身非常"稀疏"的前提下。然而实际情形中,针对大型刚体目标成像,如飞机、舰船等,其距离维及方位维通常存在一定的扩展特性,此时成像场景目标空间域的稀疏性相对较差,如果仍采用传统方法进行目标反演,所获得的目标重构性能通常并不理想。据此,基于扩展目标的先验信息,提出了一种改进的贝叶斯压缩感知成像方法。仿真试验验证了所提方法的有效性。

Most of existing compressed sensing（CS）based radar imaging methods are based on the assumption that the targets are sparse enough.While in practice the large rigid targets,such as aircrafts and ships,are often extended in range and cross range dimensions.Therefore,the sparsity of the target space in the imaging scene is relatively poor.If traditional methods are utilized to make inversion of the target,the reconstruction performances would be severely degraded.By using the sparsity and continuity property of the target,a novel Bayesian CS-based imaging method is proposed in this paper.Experimental results verify the effectiveness of the proposed method.