TIME DOMAIN SPARSE REPRESENTATION FOR MULTI-ASPECT SAR DATA OF TARGETS

Abstract

Sparse representation is the fundamental technology of compressive sensing, sparse three- dimensional (3-D) imaging, and dictionary-based parameter estimation. Typical sparse representation models of radar signal work in the frequency domain, which may encounter high dimension and large data amount of dictionary. This paper presents a time-domain (TD) representation model for multi-aspect SAR data. We generate the multi-aspect two-dimensional (2-D) TD responses of the 3-D scattering center model. Then we cut off the low-energy area of the 2-D TD response and use cutoff responses to construct the dictionary of sparse representation. Such a TD dictionary is a sparse matrix. Moreover, we build and solve the sparse representation model based on the TD dictionary. Compared with the frequency-domain (FD) sparse representation model, the data size of our TD dictionary is remarkably lower, and the solving of TD sparse representation problem is in higher efficiency. We utilize the TD sparse representation to reconstruct 3-D images from multi-aspect SAR data. Experimental results demonstrate the effectiveness and efficiency of the TD sparse representation model.