Enhanced state estimation using multiscale Kalman filtering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Multiscale wavelet-based representation of data has shown great noise removal abilities when used in data filtering. In this paper, a multiscale Kalman filtering (MSKF) algorithm is developed, in which the filtering advantages of multiscale representation are combined with those of the Kaiman filter to further enhance its estimation performance. The MSKF algorithm relies on representing the data at multiple scales using Stationary Wavelet Transform (SWT), applying Kalman filtering on the scaling coefficients at each scales, and then selecting the optimum scale at which the Kaiman filter minimizes a cross validation mean square error criterion. The multiscale state space models Used in MSKF are also derived using the SWT representation. The MSKF algorithm is shown to outperform the conventional Kalman filter through a simulated example, and the reason behind this improvement is the additional filtering advantage gained by the low pass filters used in SWT.

Original languageEnglish
Title of host publicationProceedings of the 45th IEEE Conference on Decision and Control 2006, CDC
Pages1679-1684
Number of pages6
Publication statusPublished - 1 Dec 2006
Event45th IEEE Conference on Decision and Control 2006, CDC - San Diego, CA, United States
Duration: 13 Dec 200615 Dec 2006

Other

Other45th IEEE Conference on Decision and Control 2006, CDC
CountryUnited States
CitySan Diego, CA
Period13/12/0615/12/06

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

Cite this

Nounou, M. (2006). Enhanced state estimation using multiscale Kalman filtering. In Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC (pp. 1679-1684). [4177666]