Improved DOA estimation with acoustic vector sensor arrays using spatial sparsity and subarray manifold

Abstract

The performance of DOA estimation with scalar sensor arrays using spatial sparse signal reconstruction (SSR) technique is affected by the grid spacing. In this paper, we formulate the DOA estimation with the acoustic vector sensor (AVS) arrays under SSR framework. A coarse-to-fine DOA estimation algorithm has been developed. The source spatial sparsity and the inter-relations among the manifold matrices of the AVS subarrays are jointly utilized to eliminate the grid effect in the SSR technique and the improvement of the overall DOA estimation performance is achieved at low complexity. Simulation results show that the proposed method effectively mitigates the DOA estimation bias caused by off-grid sources. Interestingly, our method gives good DOA estimation accuracy when sources are closely located.