Direction-of-arrival of strictly non-circular sources based on weighted mixed-norm minimization

Abstract

In this paper, a super-resolution direction-of-arrival (DoA) algorithm for strictly non-circular sources is introduced. The proposed algorithm is based on subspace-weighted mixed-norm minimization. Firstly, we augment the array aperture for efficiently exploiting the non-circularity of signal source. Then, we transform the augmented array matrix to the real array matrix due to the centro-Hermitian of the augmented array matrix. To this end, a subspace-weighted mixed-norm minimization problem is formulated for the DoA estimation. In the proposed algorithm, we utilize singular value decomposition (SVD) to reduce the dimension of matrix, which improves the computational efficiency. We design the weighted scheme by utilizing the orthogonality of the noise subspace and the array manifold dictionary, which increases the reliability of the sparse DoA estimation. As shown by simulations, the proposed algorithm outperforms the state-of-the-art algorithms in difficult scenarios, such as low signal-to-noise ratio, small snapshots, and correlated source. Moreover, the proposed algorithm exhibits a superior performance for the DoA estimation in the underdetermined case.

Covariance Matrix Reconstruction for Direction Finding with Nested Arrays Using Iterative Reweighted Nuclear Norm Minimization

“…Sparse signal representation (SSR) framework has attracted a great interest in direction finding [17][18][19][20][21][22][23], which exploits the spatial sparsity of the signal arriving angles. Despite these aforementioned SSR-based methods have some attractive features, there are still two problems need to be considered.…”