Two-Dimensional Radar Imaging Based on Continuous Compressed Sensing

This paper is concerned with two-dimensional high resolution radar imaging via compressed sensing (CS). The conventional compressive imaging methods usually assume that the target to be recovered is sparse on some prior known grids by discretizing a continuous imaging scope. However, this condition cannot be satisfied in real applications such as radar imaging and the mismatch between the actual sparse representation and the assumed one will degrade the performance of conventional methods considerably. To deal with this problem, this paper adopts a continuous compressed sensing (CCS) method based on atomic norm minimization which works directly in the continuous parameter space thus no modeling error exists. An efficient algorithm based on alternating direction method of multipliers is presented to solve the equivalent semidefinite programming problem. Experimental results based on both synthetic and measured data demonstrate that the proposed approach obtains improved sparse recovery accuracy compared with conventional grid-based CS method.