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 language | English |
|---|---|
| Article number | 6509484 |
| Pages (from-to) | 187-195 |
| Number of pages | 9 |
| Journal | IEEE Journal of Oceanic Engineering |
| Volume | 40 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2015 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Ocean Engineering
- Mechanical Engineering
- Electrical and Electronic Engineering
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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 journal › Review 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 -