Due to the sparsity of the space distribution of point scatterers and radar echo data, the theory of Compressed Sensing (CS) has been successfully applied in Inverse Synthetic Aperture Radar (ISAR) imaging, which can recover an unknown sparse signal from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, since the V style modulation(V-FM) signal can mitigate the ambiguity apparent in range and velocity, the dual-channel, two-dimension, compressed-sensing (2D-CS) algorithm is proposed for Bistatic ISAR (Bi-ISAR) imaging, which directly deals with the 2D signal model for image reconstruction based on solving a nonconvex optimization problem. The coupled 2D super-resolution model of the target’s echoes is firstly established; then, the 2D-SL0 algorithm is applied in each channel with different dictionaries, and the final image is obtained by synthesizing the two channels. Experiments are used to test the robustness of the Bi-ISAR imaging framework with the two-dimensional CS method. The results show that the framework is capable accurately reconstructing the Bi-ISAR image within the conditions of low SNR and low measured data.
Super-Resolution Model for a Compressed-Sensing Measurement Setup