High-Resolution Bistatic ISAR Imaging Based on Two-Dimensional Compressed Sensing

The theory of compressed sensing (CS) states that an unknown sparse signal can be accurately recovered from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, we present a new framework of high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) imaging based on CS. A phase-preserved CS approach for high-range resolution imaging is proposed. The phase of a Bi-ISAR signal can be extracted by constructing a phase-preserved Fourier basis, which is crucial to azimuth processing of Bi-ISAR imaging. After performing CS reconstruction in range, we present an improved version of CS-based cross-range imaging by combining modified Fourier basis and weighting with CS optimization. Simulated data are used to test the robustness of the Bi-ISAR imaging framework with two-dimensional (2-D) CS method. The results show that the framework is capable of accurate reconstruction of Bi-ISAR image in both range and cross-range.