Near-field source localization method based on covariance fitting

To improve the estimation accuracy of near-field source angles and ranges while reducing computational complexity, this paper proposes a near-field source localization method based on covariance fitting. First, by calculating the spatial correlation function between symmetric array elements, the angle and range information of near-field sources are decoupled. Then, the data covariance matrix is obtained via spatial smoothing. Subsequently, based on a covariance fitting criterion with a rank constraint, the data covariance matrix is reconstructed by solving a corresponding semidefinite programming problem. Finally, the root-MUSIC method is employed to estimate the angles of near-field sources. Using the angle estimates and the noise subspace of the array-received data covariance matrix, and based on the principle of the root-MUSIC method, range estimation of near-field sources is realized by solving a corresponding polynomial. Simulation results demonstrate that the proposed method improves the estimation accuracy of both angle and range for near-field sources, achieves matching between angle and range estimates, and reduces computational complexity by replacing spectral peak search with polynomial root solving.