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International Journal of Antennas and Propagation
Volume 2017, Article ID 1260601, 7 pages
https://doi.org/10.1155/2017/1260601
Research Article

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

1School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
2Key Laboratory of Wireless Sensor Networks and Communication, Shanghai Institute of Microsystem and Information Technology, Shanghai 200050, China

Correspondence should be addressed to Wei He; nc.ca.mis.liam@eh.iew

Received 9 November 2016; Accepted 9 April 2017; Published 11 May 2017

Academic Editor: María Elena de Cos Gómez

Copyright © 2017 Shuang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 -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 -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.