Exploration of ISAR Imaging Based on Compressed Sensing

Abstract

The traditional compressed sensing ISAR imaging algorithm is based on row and column stacking, the image matrix is arranged as a long vector, and then a one-dimensional compressed sensing method is used to obtain the sparse representation of the target image. The biggest problem with traditional methods is that the stacked vectors are so large that the measurement matrices are large, making the algorithms very time-consuming. In addition, the traditional algorithm in the real domain, so in the processing of ISAR imaging data, need to complex measurement matrix diagonalization of real processing results such that the expected circular constraint space into square constraint space, increases the range of spatial constraints will inevitably increase the algorithm error. In this paper, a complex domain compressed sensing algorithm for two-dimensional space is proposed. The processing result proves the feasibility of the algorithm.