An approach of DOA estimation using noise subspace weighted ℓ1 minimization

Using multiple measurement vectors (MMV), we propose an algorithm based on weighted ℓ
1
minimization for direction- of-arrival (DOA) estimation, in which the weights are obtained by exploiting the orthogonality between the noise subspace and the array manifold matrix. The proposed algorithm penalizes the nonzero entries whose indices correspond to the row support of the jointly sparse signals by smaller weights and the other entries whose indices are more likely to be outside of the row support of the jointly sparse signals by larger weights, and therefore it can encourage sparsity at the true source locations. Numerical examples prove that the proposed algorithm has better performance than existing algorithms based on regular ℓ
1
minimization.