Rotating spherical arrays for DOA estimation based on real-valued covariance matrix

Abstract

The performance of direction-of-arrival (DOA) estimation based on spherical arrays is constrained by the number of the elements. In this paper, we propose a novel rotating spherical array, which can form more virtual elements. However, more elements will cause more cost of computation. In order to decrease the computation load, a real-valued covariance matrix is constructed by the unitary transformation. Therefore, a real-valued signal subspace can be obtained by the eigenvalue decomposition of the real-valued covariance matrix. Based on this signal subspace, we use covariance-assisted matching pursuit (CAMP) to obtain DOA estimation. Simulation results demonstrate the effectiveness of the proposed method.