Modeling and estimation of multiscale stochastic processes

Authors:
K. C. Chou;S. Golden;A. S. Willsky
Affiliations:
Lab. for Inf. & Dec. Syst., MIT, Cambridge, MA, USA;Lab. for Inf. & Dec. Syst., MIT, Cambridge, MA, USA;Lab. for Inf. & Dec. Syst., MIT, Cambridge, MA, USA
Venue:
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Year:
1991

Citing 0
Cited 3

Maximum likelihood identification of multiscale stochastic models using the wavelet transform and the EM algorithm

ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Wavelet-based adaptive filtering

ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
Technical Communique: Wavelet-Based Identification of Linear Discrete-Time Systems: Robustness Issue

Automatica (Journal of IFAC)

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Abstract

The authors introduce a class of multiscale stochastic processes which are Markov in scale and which are characterized by dynamic state models evolving in scale. The models for these processes are motivated by the theory of multiscale representations and the wavelet transform. The authors formulate an optimal estimation problem based on these models, which has potential applications to sensor fusion problems where there exist data from sensors of differing resolution, and provide an efficient algorithm based on the wavelet transform. They give examples applying these models to first-order Gauss-Markov processes.