Sparse Coding-Inspired High-Resolution ISAR Imaging Using Multistage Compressive Sensing

Abstract

Sparse coding-inspired high-resolution inverse synthetic aperture radar (ISAR) imaging based on multistage compressive sensing (CS) is proposed in this paper. To achieve high cross-range resolution within a short coherent processing interval (CPI), we divide the ISAR imaging process into multiple stages, each of which can be regarded as sparse-coding processing, and the encoding and decoding matrices can be obtained by solving an optimization problem using the error backpropagation algorithm. The decoding matrix can be regarded as a sparse dictionary, and we can recover the ISAR image by exploiting the smoothed ${l_0}$ norm algorithm based on the decoding matrix and CS theory. The ISAR image is then transformed into a time-domain echo by the inverse fast Fourier transform, and the echo can be regarded as the input for the next stage. At each stage, the resolution is gradually improved until we obtain the expected high-resolution ISAR image. Thus, the signals' energy can be fully accumulated step by step, and we can obtain a less noise and focused ISAR image. Experimental results show that the proposed method can obtain higher quality ISAR images compared with other current techniques, and that it is an effective approach to ISAR imaging within a short CPI.