基于压缩感知的稀疏无源雷达成像

Sparse passive radar imaging based on compressive sensing

当窄带外辐射源数目稀少且空间分布不均匀时,通常会在无源雷达成像中产生稀疏的无规则空间谱填充,使得传统快速逆傅里叶方法(inverse fast Fourier transform,IFFT)或极坐标方法难以获得良好的目标成像效果。针对这种空间谱填充的稀疏性和非均匀性,利用压缩感知理论在处理稀疏随机采样信号重构问题上的优势,提出了稀疏无源雷达成像方法。同时通过构造传感矩阵的互相关和积累相关函数,对目标图像的可重构性进行了分析。理论分析和仿真结果表明,对具有稀疏随机空间谱特点的无源雷达成像,本文提出的成像方法是有效的。

The narrow bandwidth passive radar has the feature that the number of the non-uniform radiation sources is rare. This feature directly leads to a sparse and non-uniform spatial-spectral filling, which makes the conventional inverse fast Fourier transform （IFFT） and polar-coordinate method fail to get a good imaging per- formance. A novel passive imaging method is proposed for these features, and it fully takes advantage of the compressive sensing in signal recovery based on sparse and random sampling. The reconstruction performance of the target is analyzed by constructing the cross-correlation and cumulative coherence function of sensing matrixes. The theoretical analysis and simulation show the proposed method＇s effectiveness especially in the process of passive radar imaging for sparse and random spatial-spectral filling.