A Sparse Representation of Array Covariance Vectors for DOA Estimation with Unknown Mutual Coupling

Abstract

The effect of unknown mutual coupling can degrade the direction of arrival (DOA) estimation performance. In this paper, a sparse representation method of array covariance vectors (SRACV) based on a new data model is proposed to settle above problem for uniform linear array (ULA). In our proposed method, an efficient block sparse representation model is firstly constructed without the effect of unknown mutual coupling by using the banded complex symmetric Toeplitz structure of mutual coupling. Then a SRACV algorithm is proposed, in which the DOA estimation is estimated by searching the block sparse coefficients of the array covariance vector. The proposed method not only avoids the loss of array aperture, but also can perform well and get superior DOA estimation performance under the condition of unknown mutual coupling. Simulation experiments demonstrate that the superiority of our proposed method with unknown mutual coupling.