IEICE TRANSACTIONS on Communications
Super Resolution TOA Estimation Algorithm with Maximum Likelihood ICA Based Pre-Processing
Summary :
High-resolution time of arrival (TOA) estimation techniques have great promise for the high range resolution required in recently developed radar systems. A widely known super-resolution TOA estimation algorithm for such applications, the multiple-signal classification (MUSIC) in the frequency domain, has been proposed, which exploits an orthogonal relationship between signal and noise eigenvectors obtained by the correlation matrix of the observed transfer function. However, this method suffers severely from a degraded resolution when a number of highly correlated interference signals are mixed in the same range gate. As a solution for this problem, this paper proposes a novel TOA estimation algorithm by introducing a maximum likelihood independent component analysis (MLICA) approach, in which multiple complex sinusoidal signals are efficiently separated by the likelihood criteria determined by the probability density function (PDF) of a complex sinusoid. This MLICA schemes can decompose highly correlated interference signals, and the proposed method then incorporates the MLICA into the MUSIC method, to enhance the range resolution in richly interfered situations. The results from numerical simulations and experimental investigation demonstrate that our proposed pre-processing method can enhance TOA estimation resolution compared with that obtained by the original MUSIC, particularly for lower signal-to-noise ratios.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E96-B No.5 pp.1194-1201
- Publication Date
- 2013/05/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E96.B.1194
- Type of Manuscript
- PAPER
- Category
- Sensing
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Tetsuhiro OKANO, Shouhei KIDERA, Tetsuo KIRIMOTO, "Super Resolution TOA Estimation Algorithm with Maximum Likelihood ICA Based Pre-Processing" in IEICE TRANSACTIONS on Communications,
vol. E96-B, no. 5, pp. 1194-1201, May 2013, doi: 10.1587/transcom.E96.B.1194.
Abstract: High-resolution time of arrival (TOA) estimation techniques have great promise for the high range resolution required in recently developed radar systems. A widely known super-resolution TOA estimation algorithm for such applications, the multiple-signal classification (MUSIC) in the frequency domain, has been proposed, which exploits an orthogonal relationship between signal and noise eigenvectors obtained by the correlation matrix of the observed transfer function. However, this method suffers severely from a degraded resolution when a number of highly correlated interference signals are mixed in the same range gate. As a solution for this problem, this paper proposes a novel TOA estimation algorithm by introducing a maximum likelihood independent component analysis (MLICA) approach, in which multiple complex sinusoidal signals are efficiently separated by the likelihood criteria determined by the probability density function (PDF) of a complex sinusoid. This MLICA schemes can decompose highly correlated interference signals, and the proposed method then incorporates the MLICA into the MUSIC method, to enhance the range resolution in richly interfered situations. The results from numerical simulations and experimental investigation demonstrate that our proposed pre-processing method can enhance TOA estimation resolution compared with that obtained by the original MUSIC, particularly for lower signal-to-noise ratios.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E96.B.1194/_p
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@ARTICLE{e96-b_5_1194,
author={Tetsuhiro OKANO, Shouhei KIDERA, Tetsuo KIRIMOTO, },
journal={IEICE TRANSACTIONS on Communications},
title={Super Resolution TOA Estimation Algorithm with Maximum Likelihood ICA Based Pre-Processing},
year={2013},
volume={E96-B},
number={5},
pages={1194-1201},
abstract={High-resolution time of arrival (TOA) estimation techniques have great promise for the high range resolution required in recently developed radar systems. A widely known super-resolution TOA estimation algorithm for such applications, the multiple-signal classification (MUSIC) in the frequency domain, has been proposed, which exploits an orthogonal relationship between signal and noise eigenvectors obtained by the correlation matrix of the observed transfer function. However, this method suffers severely from a degraded resolution when a number of highly correlated interference signals are mixed in the same range gate. As a solution for this problem, this paper proposes a novel TOA estimation algorithm by introducing a maximum likelihood independent component analysis (MLICA) approach, in which multiple complex sinusoidal signals are efficiently separated by the likelihood criteria determined by the probability density function (PDF) of a complex sinusoid. This MLICA schemes can decompose highly correlated interference signals, and the proposed method then incorporates the MLICA into the MUSIC method, to enhance the range resolution in richly interfered situations. The results from numerical simulations and experimental investigation demonstrate that our proposed pre-processing method can enhance TOA estimation resolution compared with that obtained by the original MUSIC, particularly for lower signal-to-noise ratios.},
keywords={},
doi={10.1587/transcom.E96.B.1194},
ISSN={1745-1345},
month={May},}
Copy
TY - JOUR
TI - Super Resolution TOA Estimation Algorithm with Maximum Likelihood ICA Based Pre-Processing
T2 - IEICE TRANSACTIONS on Communications
SP - 1194
EP - 1201
AU - Tetsuhiro OKANO
AU - Shouhei KIDERA
AU - Tetsuo KIRIMOTO
PY - 2013
DO - 10.1587/transcom.E96.B.1194
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E96-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2013
AB - High-resolution time of arrival (TOA) estimation techniques have great promise for the high range resolution required in recently developed radar systems. A widely known super-resolution TOA estimation algorithm for such applications, the multiple-signal classification (MUSIC) in the frequency domain, has been proposed, which exploits an orthogonal relationship between signal and noise eigenvectors obtained by the correlation matrix of the observed transfer function. However, this method suffers severely from a degraded resolution when a number of highly correlated interference signals are mixed in the same range gate. As a solution for this problem, this paper proposes a novel TOA estimation algorithm by introducing a maximum likelihood independent component analysis (MLICA) approach, in which multiple complex sinusoidal signals are efficiently separated by the likelihood criteria determined by the probability density function (PDF) of a complex sinusoid. This MLICA schemes can decompose highly correlated interference signals, and the proposed method then incorporates the MLICA into the MUSIC method, to enhance the range resolution in richly interfered situations. The results from numerical simulations and experimental investigation demonstrate that our proposed pre-processing method can enhance TOA estimation resolution compared with that obtained by the original MUSIC, particularly for lower signal-to-noise ratios.
ER -
English
