An Improved Aperture and Effective Computation DOA Estimation Method Based on Covariance Differencing Technique

In this paper, an improved aperture and effective computation DOA estimation method for the presence of uncorrelated and correlated sources coexisting is presented. MUSIC algorithm is applied directly to estimate directions of the uncorrelated sources, after that, Hermitian and Toeplitz characters of uncorrelated sources covariance is utilized to eliminate the impact of the uncorrelated sources and Toeplitz noise. Eigen-decomposition is adopted to get direction information of coherent groups. Some necessary operations are considered to eliminate the cross-term affect between coherent groups. Then a virtual steering matrix is constructed, and each column of the steering matrix is regard as one snapshot of a virtual signal which contains all bearing information of one coherent group. At last, the SSIA (Spatial Smoothing with Improved Aperture) algorithm is applied. Because the SSIA is based on real-data domain, our proposed scheme has computational effectiveness. Some necessary discussions and simulations are presented to show the performance of the proposed method.