The DOA Estimation for a Mixture of Uncorrelated and Coherent Sources via Decomposing the Coprime Array

Abstract

The spatial smoothing-based and matrix reconstruction-based decorrelation algorithms are widely used to realize the accurate direction-of-arrival (DOA) estimation with the coexistence of both uncorrelated and coherent sources. However, these methods apply only to the traditional uniform linear array (ULA), of which the manifold matrix is a Vandermonde structure. In this paper, to achieve the DOA estimation of mixed signals on coprime array, the coprime array is decomposed into two independent sub-ULAs, and the received data is processed, respectively. An improved spatial differencing technique is exploited to estimate the uncorrelated and coherent sources separately, and the lost degree of freedom (DOF) caused by array decomposition is recovered. The final high-precision DOA estimation is obtained by conducting an ambiguity resolution method to the estimation results of the decomposed subarrays. Furthermore, double-phase partial spectral search scheme is introduced to diminish the computational complexity. The feasibility and superiority of the proposed algorithm are validated through detailed experimental simulations.