A covariance approximation method for near-field coherent sources localization using uniform linear array

Hoondong Noh, Chungyong Lee

Research output: Contribution to journalReview article

18 Citations (Scopus)

Abstract

The covariance approximation (CA) multiple signal classification (MUSIC) is a novel near-field direction-of-arrival (DoA) estimation method for uniform linear array. In this paper, we show that the CA-MUSIC suffers from significant performance degeneration caused by coherent sources. The CA-MUSIC with coherent sources generates the image sources (IS), which cannot be distinguished from the real sources. To solve this problem, we propose a CA-based near-field coherent sources localization algorithm, which is robust to the IS effect. The proposed CA algorithm avoids errors caused by coherence between sources using searching radius restriction and zero-forcing MUSIC. Simulation results shows that the proposed CA algorithm offers superior root mean square error (RMSE) performances for near-field coherent sources.

Original languageEnglish
Article number6509484
Pages (from-to)187-195
Number of pages9
JournalIEEE Journal of Oceanic Engineering
Volume40
Issue number1
DOIs
Publication statusPublished - 2015 Jan 1

Fingerprint

Approximation algorithms
Direction of arrival
Mean square error

All Science Journal Classification (ASJC) codes

  • Ocean Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering

Cite this

@article{0ff7ba41002048bc8227c7da02a8f27c,
title = "A covariance approximation method for near-field coherent sources localization using uniform linear array",
abstract = "The covariance approximation (CA) multiple signal classification (MUSIC) is a novel near-field direction-of-arrival (DoA) estimation method for uniform linear array. In this paper, we show that the CA-MUSIC suffers from significant performance degeneration caused by coherent sources. The CA-MUSIC with coherent sources generates the image sources (IS), which cannot be distinguished from the real sources. To solve this problem, we propose a CA-based near-field coherent sources localization algorithm, which is robust to the IS effect. The proposed CA algorithm avoids errors caused by coherence between sources using searching radius restriction and zero-forcing MUSIC. Simulation results shows that the proposed CA algorithm offers superior root mean square error (RMSE) performances for near-field coherent sources.",
author = "Hoondong Noh and Chungyong Lee",
year = "2015",
month = "1",
day = "1",
doi = "10.1109/JOE.2013.2249872",
language = "English",
volume = "40",
pages = "187--195",
journal = "IEEE Journal of Oceanic Engineering",
issn = "0364-9059",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

A covariance approximation method for near-field coherent sources localization using uniform linear array. / Noh, Hoondong; Lee, Chungyong.

In: IEEE Journal of Oceanic Engineering, Vol. 40, No. 1, 6509484, 01.01.2015, p. 187-195.

Research output: Contribution to journalReview article

TY - JOUR

T1 - A covariance approximation method for near-field coherent sources localization using uniform linear array

AU - Noh, Hoondong

AU - Lee, Chungyong

PY - 2015/1/1

Y1 - 2015/1/1

N2 - The covariance approximation (CA) multiple signal classification (MUSIC) is a novel near-field direction-of-arrival (DoA) estimation method for uniform linear array. In this paper, we show that the CA-MUSIC suffers from significant performance degeneration caused by coherent sources. The CA-MUSIC with coherent sources generates the image sources (IS), which cannot be distinguished from the real sources. To solve this problem, we propose a CA-based near-field coherent sources localization algorithm, which is robust to the IS effect. The proposed CA algorithm avoids errors caused by coherence between sources using searching radius restriction and zero-forcing MUSIC. Simulation results shows that the proposed CA algorithm offers superior root mean square error (RMSE) performances for near-field coherent sources.

AB - The covariance approximation (CA) multiple signal classification (MUSIC) is a novel near-field direction-of-arrival (DoA) estimation method for uniform linear array. In this paper, we show that the CA-MUSIC suffers from significant performance degeneration caused by coherent sources. The CA-MUSIC with coherent sources generates the image sources (IS), which cannot be distinguished from the real sources. To solve this problem, we propose a CA-based near-field coherent sources localization algorithm, which is robust to the IS effect. The proposed CA algorithm avoids errors caused by coherence between sources using searching radius restriction and zero-forcing MUSIC. Simulation results shows that the proposed CA algorithm offers superior root mean square error (RMSE) performances for near-field coherent sources.

UR - http://www.scopus.com/inward/record.url?scp=84920947818&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84920947818&partnerID=8YFLogxK

U2 - 10.1109/JOE.2013.2249872

DO - 10.1109/JOE.2013.2249872

M3 - Review article

AN - SCOPUS:84920947818

VL - 40

SP - 187

EP - 195

JO - IEEE Journal of Oceanic Engineering

JF - IEEE Journal of Oceanic Engineering

SN - 0364-9059

IS - 1

M1 - 6509484

ER -