Abstract:

Using the spatial sparsity of the source signals, a direction of arrival (DOA) estimation method is proposed based on the sparse representation of multiple measurement vectors (MMV). First, the signal subspace is estimated by taking the left singular eigenvectors of the samples matrix corresponding to the big singular eigenvalues. Then the new re-weighted iterative minimum variance (RIMV) method is adopted to the signal subspace to determine the DOA of the targets. In addition, since it does not utilize the statistic information like the conventional high-resolution methods do, the proposed method can effectively distinguish the signal sources with any coherence without decorrelation processing. Experimental results show that the method can exactly estimate the DOA of source signals and bring a great computation reduction.