Spatially Distributed Sources Localization with a Subspace Based Estimator Without Eigendecomposition

In this paper, a new subspace-based algorithm for parametric estimation of angular parameters of multiple incoherently distributed sources is proposed. This method consists of using the subspace principle without any eigendecomposition of the covariance matrix, so that it does not require the knowledge of the effective dimension of the pseudosignal subspace and then the major difficulty of the existing subspace estimators can be avoided. The proposed idea relies on the use of the property of the inverse of the covariance matrix to exploit approximately the orthogonality property between column vectors of the noise-free covariance matrix and the sample pseudo-noise subspace. Simulation results show that, compared with other known methods, the proposed algorithm exhibits a better estimation performance.