Abstract:

Utilizing the space sparsity property of radar targets, a compressive sensing based pseudorandom step frequency radar (CS-PRSFR) is studied. Firstly, the CS-PRSFR targets echo is analyzed and the targets parameter extracting model is constructed. To solve the problem of inapplicability of traditional sparse signal reconstruction algorithms amid noise of unknown statistics, a cross validation based robust SL0 (CV-RSL0) algorithm extracting the parameter of targets is proposed. Because of the better incoherence of the sensing matrix, the CS-PRSFR can obtain a higher rangevelocity joint resolution performance. The proposed algorithm needs no prior information of the noise statistics, and the performance of its targets parameter extraction can rapidly approach the lower bound of the best estimator as the signal to noise ratio improving. Simulation results illuminate the correctness and efficiency of this method.