Auxiliary-Particle-Filter-Based Two-Filter Smoothing for Wiener State-Space Models

Roland Hostettler, Thomas B. Schön

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
121 Downloads (Pure)

Abstract

In this paper, we propose an auxiliary-particle-filter-based two-filter smoother for Wiener state-space models. The proposed smoother exploits the model structure in order to obtain an analytical solution for the backward dynamics, which is introduced artificially in other two-filter smoothers. Furthermore, Gaussian approximations to the optimal proposal density and the adjustment multipliers are derived for both the forward and backward filters. The proposed algorithm is evaluated and compared to existing smoothing algorithms in a numerical example where it is shown that it performs similarly to the state of the art in terms of the root mean squared error at lower computational cost for large numbers of particles.

Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Information Fusion, FUSION 2018
PublisherIEEE
Pages1904-1911
Number of pages8
ISBN (Print)9780996452762
DOIs
Publication statusPublished - 5 Sep 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Information Fusion - Cambridge, United Kingdom
Duration: 10 Jul 201813 Jul 2018
Conference number: 21

Conference

ConferenceInternational Conference on Information Fusion
Abbreviated titleFUSION
CountryUnited Kingdom
CityCambridge
Period10/07/201813/07/2018

Keywords

  • particle filtering
  • Sequential Monte Carlo
  • state estimation
  • state-space methods
  • state-space models
  • Wiener models

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