Mixed Far-field and Near-field Source Localization Based on Low-Rank Matrix Reconstruction

Abstract

In this paper, we present a mixed far-field (FF) and near-field (NF) source localization algorithm based on low-rank matrix reconstruction (LRMR). At first, the signal covariance matrix is recovered by solving an optimization problem to obtain the signal component and suppress the noise component via LRMR. Then, according to the characteristics of the covariance matrix of a symmetric uniform linear array, the 2D parameters of range and direction-of-arrival (DOA) are separated, in which the range is eliminated by extracting the inverse diagonal elements of the reconstructed matrix. The extracted elements are reconstructed into a Toeplitz matrix, and the DOAs of the FF and NF sources are estimated. Finally, on the basis of the estimated DOA, the range parameter of the NF source is obtained. The proposed method can distinguish different types of the sources. The simulation results show that it has higher estimation accuracy than the conventional subspace-based methods in relatively low SNR, and can obtain automatic parameter pairing of the DOA and range for multiple mixed sources.