| 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 |