An Improved L1-SVD Algorithm Based on Noise Subspace for Doa Estimation

Abstract

Abstract-In this paper, an improved L 1 -SVD algorithm based on noise subspace is presented for direction of arrival (DOA) estimation using reweighted L 1 norm constraint minimization. In the proposed method, the weighted vector is obtained by utilizing the orthogonality between noise subspace and signal 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 robustness to noise, but also enhances resolution in DOA estimation. Furthermore, it is not very sensitive to the incorrect determination of the number of signals and can primely suppress spurious peak in DOA estimation. Simulation results are shown that the presented algorithm has better performance than the existing algorithms, such as MUSIC, L 1 -SVD algorithm.

An effective wideband spectrum sensing method based on L1-SVD algorithm

“…The matrices Y,X,W in the above equation have the following fonns According to [15], the Ll-SVD method can be concluded as follows.…”

“…Xsv where 11 is the K x 1 vector, whose entries are defined to be the 2-norm of the corresponding rows of XSl" f3 is the pre-given regularization parameter [15].…”

Gridless DOA estimation based on multivariate function genetic optimisation

“…Considering a uniform linear array, 100 Monte Carlo experiments are done to eliminate randomness. l 1 ‐SVD [13] and orthogonal matching pursuit (OMP) [14] algorithms are taken for performance comparison.…”

Direction Estimation of Correlated/Coherent Signals by Sparsely Representing the Signal-Subspace Eigenvectors

“…where λ m is the mth largest eigenvalue of R. Eq. (16) indicates that |c i,m | 1 when the number of snapshots is adequate. Then the ith eigenvectorû i can be derived by normalizing u i , which is approximately given as follows by neglecting the higher-than-secondorder terms,…”

“…L1-SVD contributes much to the development of the sparsity-inducing DOA estimation techniques, and it also adapts well to signal correlation. For correlated signals, although the borderline between the signaland noise-subspaces becomes vague, most of the signal energy is still contained in the signal-subspace eigenvectors, so L1-SVD shows satisfying adaptation to signal correlation [15,16]. More recently, Hyder and Mahata introduced their joint sparsity-enforcing technique to DOA estimation, and proposed the method of JLZA-DOA [17].…”