Title :
Wavelets and dynamic pattern recognition
Author :
Pan, Zuohong ; Wang, Xiaodi
Author_Institution :
Western Connecticut State Univ., Danbury, CT, USA
Abstract :
The spectral analysis in the spirit of traditional Fourier transform does not preserve the time dependence of the patterns when a signal is nonstationary. Wavelet analysis, on the other hand, has emerged as a remarkable tool for decomposition of functions. General procedures of wavelet-based regression estimators assume the time-invariant coefficients. To accommodate the stochastic and dynamic properties that are typical of many applications, we incorporate the state-space model in the wavelet estimator. The coefficients of the wavelet estimators are formulated as dynamic (random) processes so that the Kalman filtering approach can be applied. The resulting estimator is a stochastic nonlinear wavelet-based estimator
Keywords :
Kalman filters; filtering theory; pattern recognition; random processes; state-space methods; statistical analysis; stochastic processes; wavelet transforms; Fourier transform; Kalman filtering; dynamic pattern recognition; dynamic processes; dynamic properties; nonstationary signal; random processes; spectral analysis; state-space model; stochastic nonlinear wavelet based estimator; stochastic properties; time dependence; time-invariant coefficients; wavelet analysis; wavelet based regression estimators; wavelet estimator coefficients; Discrete wavelet transforms; Fourier transforms; Kalman filters; Pattern recognition; Signal processing; Signal processing algorithms; Spectral analysis; State estimation; Stochastic processes; Wavelet analysis;
Conference_Titel :
Signal Processing, 1996., 3rd International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-2912-0
DOI :
10.1109/ICSIGP.1996.566536
