Reweighted -norm minimisation for high-resolution DOA estimation under unknown mutual coupling
A reweighted -norm minimisation algorithm for high-resolution direction-of-arrival (DOA) estimation under unknown mutual coupling is proposed in this Letter. First, the proposed method forms a new block representation model by parameterising the steering vector. Then, a reweighted -norm constraint based on the new data model is proposed, in which the principle of a novel multiple signals classification (MUSIC)-like algorithm is used to construct a weighted matrix. Finally, the DOAs can be achieved by the recovered sparse matrix. Owing to the use of the whole received data and reweighted procedure, the performance of the proposed method is better than the state-of-the-art methods. Extensive simulation experiments confirm that the above inference is correct.
