Compressed Sensing Radar Imaging Methods Based on Modified Orthogonal Matching Pursuit Algorithms

High computational complexity is a problem that radar imaging technique based on Compressed Sensing (CS) must overcome for practical applications. In the light of the sparsity of radar target reflectivity, this paper studies 2D joint compressive imaging methods based on modified Orthogonal Matching Pursuit (OMP) algorithms. The sparse representation model of stepped frequency radar echo is established and analyzed, according to the 2D separability of sparse dictionary and compressive measurement, an improved OMP algorithm is proposed for radar image formation, which improves the computational efficiency greatly and can be extended to other greedy algorithms easily. Theoretical comparison and analysis indicate that the proposed methods possess prominent superiority over storage and computation compared to conventional CS algorithms, experiments from both simulated data and measured data verify their validity.