Compressive Sensing ISAR imaging with LFM signal

Compressive sensing (CS) theory has been recently used to obtain high-resolution inverse synthetic aperture radar (ISAR) images. In this paper a compressive sensing algorithm is used to reconstruct the high-resolution ISAR image with limited measurements for linear frequency modulated (LFM) signal. In order to speed up the reconstruction and reduce the memory usage, we exploit a two-dimensional (2D) optimization method to solve the sparsity-driven optimization problem. Finally, experimental results using real data of an airplane and a ship show that the approach outperforms the conventional range Doppler algorithm (RDA).