IEICE TRANSACTIONS on Fundamentals
Proposal of the Fast Kernel MUSIC Algorithm
Summary :
It is an important problem in fields of radar, sonar, and so on to estimate parameters of closely spaced multiple signals. The MUSIC algorithm with the forward-backward (FB) spatial smoothing is considered as the most effective technique at present for the problem with coherent signals in a variety of fields. We have applied this in Laser Microvision. Recently, Shimotahira has proposed the Kernel MUSIC algorithm, which is applicable to cases when signal vectors and noise vectors are orthogonal. It also utilizes Gaussian elimination of the covariance matrix instead of eigenvalue analysis to estimate noise vectors. Although the amount of computation by the Kernel MUSIC algorithm became lighter than that of the conventional MUSIC algorithm, the covariance matrix was formed to estimate noise vectors and also all noise vectors were used to analyze the MUSIC eigenspectrum. The heaviest amount of computation in the Kernel MUSIC algorithm exists in the transformation of the covariance matrix and the analysis of the MUSIC eigenspectrum. We propose a more straightforward algorithm to estimate noise vectors without forming a covariance matrix, easier algorithm to analyze the MUSIC eigenspectrum. The superior characteristics will be demonstrated by results of numerical simulation.
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- IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.8 pp.1232-1239
- Publication Date
- 1996/08/25
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- Special Section PAPER (Special Section on Digital Signal Processing)
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Fumie TAGA, Hiroshi SHIMOTAHIRA, "Proposal of the Fast Kernel MUSIC Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E79-A, no. 8, pp. 1232-1239, August 1996, doi: .
Abstract: It is an important problem in fields of radar, sonar, and so on to estimate parameters of closely spaced multiple signals. The MUSIC algorithm with the forward-backward (FB) spatial smoothing is considered as the most effective technique at present for the problem with coherent signals in a variety of fields. We have applied this in Laser Microvision. Recently, Shimotahira has proposed the Kernel MUSIC algorithm, which is applicable to cases when signal vectors and noise vectors are orthogonal. It also utilizes Gaussian elimination of the covariance matrix instead of eigenvalue analysis to estimate noise vectors. Although the amount of computation by the Kernel MUSIC algorithm became lighter than that of the conventional MUSIC algorithm, the covariance matrix was formed to estimate noise vectors and also all noise vectors were used to analyze the MUSIC eigenspectrum. The heaviest amount of computation in the Kernel MUSIC algorithm exists in the transformation of the covariance matrix and the analysis of the MUSIC eigenspectrum. We propose a more straightforward algorithm to estimate noise vectors without forming a covariance matrix, easier algorithm to analyze the MUSIC eigenspectrum. The superior characteristics will be demonstrated by results of numerical simulation.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_8_1232/_p
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@ARTICLE{e79-a_8_1232,
author={Fumie TAGA, Hiroshi SHIMOTAHIRA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Proposal of the Fast Kernel MUSIC Algorithm},
year={1996},
volume={E79-A},
number={8},
pages={1232-1239},
abstract={It is an important problem in fields of radar, sonar, and so on to estimate parameters of closely spaced multiple signals. The MUSIC algorithm with the forward-backward (FB) spatial smoothing is considered as the most effective technique at present for the problem with coherent signals in a variety of fields. We have applied this in Laser Microvision. Recently, Shimotahira has proposed the Kernel MUSIC algorithm, which is applicable to cases when signal vectors and noise vectors are orthogonal. It also utilizes Gaussian elimination of the covariance matrix instead of eigenvalue analysis to estimate noise vectors. Although the amount of computation by the Kernel MUSIC algorithm became lighter than that of the conventional MUSIC algorithm, the covariance matrix was formed to estimate noise vectors and also all noise vectors were used to analyze the MUSIC eigenspectrum. The heaviest amount of computation in the Kernel MUSIC algorithm exists in the transformation of the covariance matrix and the analysis of the MUSIC eigenspectrum. We propose a more straightforward algorithm to estimate noise vectors without forming a covariance matrix, easier algorithm to analyze the MUSIC eigenspectrum. The superior characteristics will be demonstrated by results of numerical simulation.},
keywords={},
doi={},
ISSN={},
month={August},}
Copy
TY - JOUR
TI - Proposal of the Fast Kernel MUSIC Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1232
EP - 1239
AU - Fumie TAGA
AU - Hiroshi SHIMOTAHIRA
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1996
AB - It is an important problem in fields of radar, sonar, and so on to estimate parameters of closely spaced multiple signals. The MUSIC algorithm with the forward-backward (FB) spatial smoothing is considered as the most effective technique at present for the problem with coherent signals in a variety of fields. We have applied this in Laser Microvision. Recently, Shimotahira has proposed the Kernel MUSIC algorithm, which is applicable to cases when signal vectors and noise vectors are orthogonal. It also utilizes Gaussian elimination of the covariance matrix instead of eigenvalue analysis to estimate noise vectors. Although the amount of computation by the Kernel MUSIC algorithm became lighter than that of the conventional MUSIC algorithm, the covariance matrix was formed to estimate noise vectors and also all noise vectors were used to analyze the MUSIC eigenspectrum. The heaviest amount of computation in the Kernel MUSIC algorithm exists in the transformation of the covariance matrix and the analysis of the MUSIC eigenspectrum. We propose a more straightforward algorithm to estimate noise vectors without forming a covariance matrix, easier algorithm to analyze the MUSIC eigenspectrum. The superior characteristics will be demonstrated by results of numerical simulation.
ER -
English
