Velocity false target identification in random pulse initial phase radar based on compressed sensing

The random pulse initial phase radar has potential applications in suppressing digital radio frequency memory (DRFM) repeat jamming, due to the significant differences between the repeat jamming and the true target echo in phases. However, owing to phase randomness and existence of false targets, the high sidelobe pedestal generated in correlation processing will decrease the performance of target detecting. Considering the low sidelobe characteristic under the framework of compressed sensing (CS), a novel sparsity-driven strategy for false target identification is proposed in this paper. Since the differences mentioned above will induce that the echoes of true and false targets are sparse in different dictionaries, the radar echo can be sparse represented in the union of two kinds of dictionaries, and the true and false targets can then be separated. According to the simulation results, we can see that the proposed method shows high performance on detecting targets and high accuracy in evaluating the motion parameters of both the true and false targets.