15 April 1983 Pattern Recognition Using Wigner Distribution Function
Author Affiliations +
Proceedings Volume 0422, 10th Intl Optical Computing Conf; (1983); doi: 10.1117/12.936140
Event: 10th International Optical Computing Conference, 1983, Cambridge, United States
Abstract
The Wigner Distribution Function (WDF) is an excellent tool for characterizing time varying signals. Several approaches have been recently suggested for optical implementation of Wigner Distri-bution Function of signals. In this paper, we report on our simulation efforts to use WDF for signal and image classification. We also relate WDF to other conventional time-frequency representa-tions of a signal such as Ambiguity Function. We develop analytical results quantifying the performance of WDF in detection problems. These analytical results are compared to the experimentally observed results. Signal parameter estimation methods that use WDF are also introduced. Extensions of WDF approach to higher dimensions (as in the case of images) are briefly outlined.
© (1983) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
B.V.K. Vijaya Kumar, Christopher Carroll, "Pattern Recognition Using Wigner Distribution Function", Proc. SPIE 0422, 10th Intl Optical Computing Conf, (15 April 1983); doi: 10.1117/12.936140; https://doi.org/10.1117/12.936140
PROCEEDINGS
7 PAGES


SHARE
KEYWORDS
Wigner distribution functions

Signal detection

Interference (communication)

Signal to noise ratio

Pattern recognition

Signal processing

Fourier transforms

RELATED CONTENT

Harmogram feature sets for 1D and 2D data
Proceedings of SPIE (July 17 1998)
Fast nondyadic shift-invariant Gabor wavelets
Proceedings of SPIE (April 06 1995)
High-order statistics for sinusoid peak detection
Proceedings of SPIE (June 07 1995)

Back to Top