Publication detail

A Rao-Blackwellized particle filter to estimate the time-varying noise parameters in non-linear state-space models using alternative stabilized forgetting

PAPEŽ, M.

Original Title

A Rao-Blackwellized particle filter to estimate the time-varying noise parameters in non-linear state-space models using alternative stabilized forgetting

Type

conference paper

Language

English

Original Abstract

The identification of slowly-varying noise parameters in non-linear state-space models constitutes a long-standing problem. The present paper addresses this task using the Bayesian framework and sequential Monte Carlo (SMC) methodology. The proposed approach utilizes an algebraic structure of the model so that the Rao-Blackwellization of the parameters can be performed, thus involving a finite-dimensional sufficient statistic for each particle trajectory into the resulting algorithm. However, relying on standard SMC methods, such techniques are known to suffer from the particle path degeneracy problem. To counteract this issue, it is proposed to use alternative stabilized forgetting, which compensates for the incomplete knowledge of a model of parameter variations by finding a compromise between possible predictive densities of the parameters. An experimental study proves the efficiency of the introduced Rao-Blackwellized particle filter (RBPF) compared to some recently proposed approaches.

Keywords

Non-linear state-space models, sequential Monte Carlo, Rao-Blackwellized particle filter, recursive Bayesian parameter estimation, parameter identification

Authors

PAPEŽ, M.

Released

12. 12. 2016

Publisher

Institute of Electrical and Electronics Engineers

Location

Limassol

ISBN

978-1-5090-5844-0

Book

Proceedings of the 16th International Symposium on Signal Processing and Information Technology, ISSPIT 2016

Pages from

229

Pages to

234

Pages count

6

BibTex

@inproceedings{BUT131672,
  author="Milan {Papež}",
  title="A Rao-Blackwellized particle filter to estimate the time-varying noise parameters in non-linear state-space models using alternative stabilized forgetting",
  booktitle="Proceedings of the 16th International Symposium on Signal Processing and Information Technology, ISSPIT 2016",
  year="2016",
  pages="229--234",
  publisher="Institute of Electrical and Electronics Engineers",
  address="Limassol",
  doi="10.1109/ISSPIT.2016.7886040",
  isbn="978-1-5090-5844-0"
}