A novel DOA matrix method for coherent signals based on first order statistics

Abstract

In this paper, a novel DOA matrix method based on first order statistics is proposed, which can estimate two-dimensional (2-D) direction of arrival (DOA) for coherent signals at the cost of less computational complexity. Firstly, the pseudo autocovariance matrix and pseudo cross covariance matrix are constructed by the first order statistics of array received data, and the rank of the matrix is proved to be determined merely by the number of signals and has no relationship with the coherency of signals. Then the 2-D DOA estimation can be achieved by performing eigenvalue decomposition of the reconstructed DOA matrix. Neither spectral peak searching nor spatial smoothing are required in this method, and parameters can be paired automatically. Without any correlation operations and covariance matrix estimation, the method can exhibit a good real-time performance than conventional subspace-based algorithm. Simulation results demonstrated that the proposed method performs better than spatial smoothing DOA matrix(SS-DOAM) method with lower computational complexity.