Newtonalized Orthogonal Matching Pursuit for Mixed Far-Field and Near-Field Source Localization

Qi Zhang; Hong Jiang; Yunchang Liu

ICASSP 2024accepted0 citations

Newtonalized Orthogonal Matching Pursuit for Mixed Far-Field and Near-Field Source Localization

Abstract

This paper presents a mixed far-field (FF) and near-field (NF) source localization method based on Newtonalized orthogonal matching pursuit (NOMP). First, the orthogonal matching pursuit (OMP) algorithm is used to coarsely estimate the angles and ranges of the mixed sources, then Newton refinement is applied to locally refine the previous estimates. In the refinement stage, the 1-D NOMP and 2-D NOMP algorithms are presented to refine the parameters of the FF and NF sources, respectively. The proposed method can achieve off-grid estimation and solve the potential grid-mismatch problem. Unlike most of the existing mixed source localization methods, it can separate and localize the mixed sources with the half-wavelength spacing linear array. The simulation results show that it has higher accuracy compared with several existing methods.

BibTeX

@inproceedings{icassp2024_newtonalizedorth,
  title = {Newtonalized Orthogonal Matching Pursuit for Mixed Far-Field and Near-Field Source Localization},
  author = {Qi Zhang and Hong Jiang and Yunchang Liu},
  booktitle = {ICASSP 2024},
  year = {2024}
}