Data-adaptive regularization for DOA estimation using sparse spectrum fitting

Regularization parameter selection is critical to the performance of many sparsity-exploiting Direction-Of-Arrival (DOA) estimation algorithms. In this paper, we propose an automatic selector for choosing this parameter in the DOA estimation algorithm, which is based on the analysis of its optimality conditions. This selector requires very limited prior information and is computationally efficient. Through simulation examples, the effectiveness and robustness of the selector are illustrated.