Publication detail

Bayesian comparison of Kalman filters with known covariance matrices

DOKOUPIL, J. PAPEŽ, M. VÁCLAVEK, P.

Original Title

Bayesian comparison of Kalman filters with known covariance matrices

Type

conference paper

Language

English

Original Abstract

A growing-window recursive procedure for model comparison is proposed based on the Bayesian inference principle. This procedure, compared to the batch one, is capable of processing unlimited increases in the uncertainty of the initial parameter settings, which is a characteristic of Kalman type algorithms. The present paper applies the suggested procedure to assess the degree of support for the state point estimates generated by multiple Kalman filters. We investigate a case where the covariance of the measurement noise and the normalized covariance matrix of the process noise are both available.

Keywords

Kalman filter, Bayesian methods, model comparison

Authors

DOKOUPIL, J.; PAPEŽ, M.; VÁCLAVEK, P.

RIV year

2015

Released

10. 3. 2015

ISBN

978-0-7354-1287-3

Book

AIP conference proceedings

ISBN

0094-243X

Periodical

AIP conference proceedings

Year of study

1648

State

United States of America

Pages from

1

Pages to

4

Pages count

4

URL

http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.4912383

BibTex

@inproceedings{BUT117758,
  author="Jakub {Dokoupil} and Milan {Papež} and Pavel {Václavek}",
  title="Bayesian comparison of Kalman filters with known covariance matrices",
  booktitle="AIP conference proceedings",
  year="2015",
  journal="AIP conference proceedings",
  volume="1648",
  pages="1--4",
  doi="10.1063/1.4912383",
  isbn="978-0-7354-1287-3",
  issn="0094-243X",
  url="http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.4912383"
}