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Path integrals and finite dimensional filters

Published online by Cambridge University Press:  04 August 2010

S. J. Maybank
Affiliation:
Robotics Research Laboratory, University of Oxford. Department of Engineering Science, 19 Parks Road, Oxford OX1 3PJ, UK.
Alison Etheridge
Affiliation:
University of Edinburgh
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Summary

Abstract

A path integral representation is obtained for the optimal probability density function of the system state, conditional on the measurements. For certain non-linear systems the optimal density can be evaluated recursively using only a finite number of statistics. These systems extend the class found by Beneš, in that the drift need not be the gradient of a scalar potential.

In the one dimensional case the trajectories of the deterministic system underlying the Beneš filter fall into five classes according to their behaviour as t → ∞. It is shown that an arbitrary deterministic trajectory can be approximated at small times to an accuracy of O(t5) by a trajectory for which the Benes filter is appropriate.

Introduction

The Kalman-Bucy filter and its many variations are widely used throughout science and engineering for estimating the state of time varying systems. Applications to computer vision in particular are described in. A typical filter contains a model of the system dynamics and of the measurement process. It is given a sequence of measurements obtained over an extended time, and produces from this sequence an estimate of the probability density function for the system state, conditional on the measurements. If the filter is optimal, then the estimated density is the exact conditional density. The best known optimal filter is the Kalman-Bucy filter.

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Publisher: Cambridge University Press
Print publication year: 1995

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  • Path integrals and finite dimensional filters
    • By S. J. Maybank, Robotics Research Laboratory, University of Oxford. Department of Engineering Science, 19 Parks Road, Oxford OX1 3PJ, UK.
  • Edited by Alison Etheridge, University of Edinburgh
  • Book: Stochastic Partial Differential Equations
  • Online publication: 04 August 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511526213.014
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  • Path integrals and finite dimensional filters
    • By S. J. Maybank, Robotics Research Laboratory, University of Oxford. Department of Engineering Science, 19 Parks Road, Oxford OX1 3PJ, UK.
  • Edited by Alison Etheridge, University of Edinburgh
  • Book: Stochastic Partial Differential Equations
  • Online publication: 04 August 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511526213.014
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Path integrals and finite dimensional filters
    • By S. J. Maybank, Robotics Research Laboratory, University of Oxford. Department of Engineering Science, 19 Parks Road, Oxford OX1 3PJ, UK.
  • Edited by Alison Etheridge, University of Edinburgh
  • Book: Stochastic Partial Differential Equations
  • Online publication: 04 August 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511526213.014
Available formats
×