Localization of Near-Field Sources Based on Sparse Signal Reconstruction with Regularization Parameter Selection

Abstract

Source localization using sensor array in the near-field is a two-dimensional nonlinear parameter estimation problem which requires jointly estimating the two parameters: direction-of-arrival and range. In this paper, a new source localization method based on sparse signal reconstruction is proposed in the near-field. We first utilize l1-regularized weighted least-squares to find the bearings of sources. Here, the weight is designed by making use of the probability distribution of spatial correlations among symmetric sensors of the array. Meanwhile, a theoretical guidance for choosing a proper regularization parameter is also presented. Then one well-known l1-norm optimization solver is employed to estimate the ranges. The proposed method has a lower variance and higher resolution compared with other methods. Simulation results are given to demonstrate the superior performance of the proposed method.