压缩感知在超宽带雷达成像中的应用

Compressive sensing for ultra-wideband radar imaging

利用信号的先验稀疏性,通过压缩感知(Compressive Sensing,CS)方法可以实现从少量的非适应性随机测量数据重建原始信号。将压缩感知理论应用到超宽带雷达高分辨率成像中,提出基于CS理论的二维方位-距离向成像算法,可以显著减少数据采集时间、数据量、处理时间以及节省信号带宽,并利用矢量网络分析仪(Vector Network Analyzer,VNA)测量的实验数据验证了采用时间和空间减采样数据的CS算法可以实现与传统的延迟-求和波束形成方法(Delay-Sum Beamform-ing,DSBF)相当的成像质量和分辨率。

Compressive sensing(CS)asserts that one can recover original signals from far fewer nonadaptive and random samples or measurements under the condition of sparse signals. This paper introduces the CS theory into the UWB radar high-resolution imaging,then presents a 2-D cross-range imaging algorithm based on CS,which can reduce the acquisition time,data storage,processing time and save signal bandwidth. Using actual experimental data of vector network analyzer(VNA),it is shown that the proposed algorithm usin...