W-l1-sracv algorithm for direction-of- arrival estimation

Abstract

This paper presents an effective weighted-Li-sparse representation of array covariance vectors (W-L1-SRACV) algorithm which exploits compressed sensing theory for direction-of-arrival (DOA) estimation of multiple narrow-band sources impinging on the far field of a uniform linear array (ULA). Based on the sparse representation of array covariance vectors, a weighted L1-norm minimization is applied to the data model, in which the weighted vector can be obtained by taking advantage of the orthogonality between the noise subspace and the signal subspace. By searching the sparsest coefficients of the array covariance vectors simultaneously, DOAs can be effectively estimated. Compared with the previous works, the proposed method not only has a super-resolution but also improves the robustness in low SNR cases. Furthermore, it can effectively suppresses spurious peaks which will disturb the correct judgment of real signal peak in the signal recovery processing. Simulation results are shown to demonstrate the efficacy of the presented algorithm.