THE HIGH RESOLUTION MIMO RADAR SYSTEM BASED ON MINIMIZING THE STATISTICAL COHERENCE OF COMPRESSED SENSING MATRIX

Compressed Sensing （CS） theory is a great breakthrough of the traditional Nyquist sam- pling theory. It can accomplish compressive sampling and signal recovery based on the sparsity of in- terested signal, the randomness of measurement matrix and nonlinear optimization method of signal recovery. Firstly, the CS principle is reviewed. Then the ambiguity function of Multiple-Input Multi- ple-Output （MIMO） radar is deduced. After that, combined with CS theory, the ambiguity function of MIMO radar is analyzed and simulated in detail. At last, the resolutions of coherent and non-coherent MIMO radars on the CS theory are discussed. Simulation results show that the coherent MIMO radar has better resolution performance than the non-coherent. But the coherent ambiguity function has higher side lobes, which caused a deterioration in radar target detection performances. The stochastic embattling method of sparse array based on minimizing the statistical coherence of sensing matrix is proposed. And simulation results show that it could effectively suppress side lobes of the ambiguity function and improve the capability of weak target detection.