Sparse representation-based joint angle and Doppler frequency estimation for MIMO radar

An algorithm based on sparse representation for joint angle and Doppler frequency estimation in multiple-input multiple-output radar is proposed. Through the data reconstruction, the algorithm only requires the dictionary for one-dimensional angle [e.g. direction of departure (DOD)], which reduces the computational complexity compared to conventional method using dictionary for two-dimensional angle. The DOD can be estimated by finding the non-zero rows in the recovered matrix, which also contains the information of the direction of arrival (DOA) and the Doppler frequency, and they can be achieved via singular value decomposition and least squares (LS) principle. The estimated DOD, DOA and Doppler frequency can be automatically paired and the parameter estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-based algorithm and parallel factor (PARAFAC) method. Furthermore, the proposed algorithm requires no knowledge of the number of targets and works well for coherent targets. Simulation results verify the effectiveness of the algorithm.