volume | PIER Journals

Abstract

In this paper, an improved L1-SVD algorithm based on noise subspace is presented for direction of arrival (DOA) estimation using the reweighted L1 minimization. In the proposed method, the weighted vector is obtained by utilizing the orthogonality between the noise subspace and the subspace spanned by the array manifold matrix. The presented algorithm banishes the nonzero entries whose indices are inside of 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. Therefore, the sparsity at the real signal locations can be enhanced by using the presented method. The proposed approach offers a good deal of merits over other DOA techniques. It not only increases the robustness to noise, but also enhances resolution in DOA estimation. Furthermore, it does not require an exact initialization. Simulation results show that the presented algorithm has better performance than the existing algorithms, such as MUSIC, L1-SVD algorithm.

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Prof. Fulai Liu

Dr. Lu Peng

Dr. Ming Wei

Dr. Pingping Chen

Dr. Shouming Guo