Partitioned Update Binomial Gaussian Mixture Filter

Matti Raitoharju, Angel F. Garcia-Fernandez, Simo Sarkka

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

Abstract

Gaussian Mixture Filters (GMFs) are approximations of the Bayesian filter for nonlinear estimation. A GMF consists of a weighted sum of Gaussian components. Each component is propagated and updated with a Kalman-type filter. When the nonlinearity is small in the update step, the required number of components to yield an accurate approximation is small and vice versa. In this paper, we propose multiple improvements to GMF that reduce the computational load and increase the estimation accuracy. The new filter processes measurements so that the least nonlinear measurements will be applied first, this reduces the need for new components. After splitting a Gaussian component, the update is done so that the measurement function is evaluated only in nonlinear directions, which reduces computational load. Finally we propose a new faster algorithm for reducing the number of components after measurements are applied. Results show that the proposed improvements make the algorithm faster and improve the estimation accuracy with respect to a GMF that is used as a basis for development.

Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Information Fusion, FUSION 2019
PublisherIEEE
Number of pages8
ISBN (Electronic)9780996452786
Publication statusPublished - 1 Jul 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Information Fusion - Ottawa, Canada
Duration: 2 Jul 20195 Jul 2019
Conference number: 22

Conference

ConferenceInternational Conference on Information Fusion
Abbreviated titleFUSION
CountryCanada
CityOttawa
Period02/07/201905/07/2019

Keywords

  • Bayesian estimation
  • Gaussian Mixture Filter
  • Kalman filters
  • Nonlinear estimation

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