Tracking nonstationarities with a wavelet transform

Authors:
H. Krim;J.-C. Pesquet;K. Drouiche
Affiliations:
CDSP center, ECE Dept., Northeastern University, Boston, MA;LSS, CNRS, UPS, ESE, Gif sur Yvette Cedex, France;ENST, Paris Cedex 13, France
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Year:
1993

Citing 2
Cited 0

Time series: theory and methods

Time series: theory and methods
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

Nonstationary signal parameter estimation/detection is challenging on account of the underlying stationarity assumption in most of the known classical techniques. We present a framework for a class of nonstationary processes via a multiscale analysis. This framework allows us to gain insight into the problem, and we obtain new results on multiscale ARIMA processes. We show the possibility of inducing stationarity at different resolution levels of nonstationary processes by an appropriate wavelet transform. This permits us to use classical estimation/detection techniques. The approach is also extended to wavelet packet decompositions.