Joint Smoothed l(0)-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar

Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed [Formula: see text]-norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed [Formula: see text]-norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the [Formula: see text]-norm minimization based methods, such as [Formula: see text]-SVD (singular value decomposition), RV (real-valued) [Formula: see text]-SVD and RV [Formula: see text]-SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance.