Mixed near-field and far-field sources localization using uniform linear array

Kai Wang, Ling Wang, Zhaolin Zhang, Jian Xie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Based on the polynomial decomposing method and high-order cumulant (HOC) technique, a novel localization algorithm for the mixed near-field and far-field sources is proposed by using uniform linear array (ULA). Firstly, the ULA is divided into two sub-arrays, with different phase reference points. Three special fourth-order cumulant matrices are designed to eliminate the range parameters of the near field sources in the steering vectors, which only contain the direction of arrival (DOA) information. Secondly, based on the ESPRIT algorithm, the DOA of each source at the phase reference point is estimated. Thirdly, with the DOA estimation, the type of the sources is classified by computing its coefficient matrix. Finally, the range parameters of near-field sources and the DOAs of far-field sources are captured. The proposed algorithm does not require any spectral search which leads to low computational complexity. Moreover, this algorithm avoids parameter matching procedure. Numerical experiments are conducted to verify the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publication30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017
PublisherInstitute of Navigation
Pages479-489
Number of pages11
ISBN (Electronic)9781510853317
DOIs
StatePublished - 2017
Event30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017 - Portland, United States
Duration: 25 Sep 201729 Sep 2017

Publication series

Name30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017
Volume1

Conference

Conference30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017
Country/TerritoryUnited States
CityPortland
Period25/09/1729/09/17

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