Time-frequency analysis and pattern recognition using singular value decomposition of the Wigner-Ville distribution

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Time-frequency analysis and pattern recognition using singular value decomposition of the Wigner-Ville distribution

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dc.contributor.author Boashash, B
dc.contributor.author Lovell, B
dc.contributor.author White, L.B.
dc.date.accessioned 2012-06-17T05:58:51Z
dc.date.available 2012-06-17T05:58:51Z
dc.date.issued 1987
dc.identifier.citation Advanced Algorithms and Architectures for Signal Processing, SPIE Conference Proceedings, vol.826, pp104-114 en_US
dc.identifier.uri http://hdl.handle.net/10576/10829
dc.description This paper presents a modified Wigner-Ville Distribution (MWVD) using Auto-regressive spectral analysis to improve the spectral resolution; it also presents descriptors which highlight a signal's FM characteristics and a data clustering algorithm for classification. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).
dc.description.abstract Time-Frequency analysis based on the Wigner-Ville Distribution (WVD) is shown to be optimal for a class of signals where the variation of instantaneous frequency is the dominant characteristic. Spectral resolution and instantaneous frequency tracking is substantially improved by using a Modified WVD (MWVD) based on an Autoregressive spectral estimator. Enhanced signal-to-noise ratio may be achieved by using 2D windowing in the Time-Frequency domain. The WVD provides a tool for deriving descriptors of signals which highlight their FM characteristics. These descriptors may be used for pattern recognition and data clustering using the methods presented in this paper. en_US
dc.language.iso en en_US
dc.publisher SPIE en_US
dc.subject pattern recognition en_US
dc.subject singular value decomposition en_US
dc.subject Wigner-Ville Distribution en_US
dc.subject Signal processing en_US
dc.subject clustering en_US
dc.subject time-frequency signal processing
dc.subject time-frequency resolution
dc.subject time-frequency concentration
dc.subject time-frequency domain
dc.subject Modified WVD
dc.subject Wigner-Ville Distribution
dc.subject time-frequency analysis
dc.subject asymptotic signals
dc.subject instantaneous frequency
dc.subject time-delay
dc.subject group delay
dc.subject discrete WVD
dc.subject linear FM signal
dc.subject hyperbolic signal
dc.subject t-f filtering;
dc.subject autoregressive
dc.subject clustering
dc.subject quadratic TFD
dc.subject SVD
dc.title Time-frequency analysis and pattern recognition using singular value decomposition of the Wigner-Ville distribution en_US
dc.type Article en_US

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