An adaptive particle filter based on the mixing probability

Yanbo Yang, Jie Zou, Feng Yang, Quan Pan

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

2 Scopus citations

Abstract

In the stochastic system whose state is described by the multiple model particle filter, the true dynamic system motion cannot be reflected precisely by the predicted measurement from each model in time because of random sampling. It causes the probability of each model inaccuracy and falls down the performance of estimation. In the interacting multiple model particle filter (IMMPF) algorithm, the dominant model should be paid more attention in order to close the posterior. So it should have much more sampling particles. Meanwhile, it is not necessary to utilize too many particles in other models which perform weak. Considered the above problem, an improved method for the IMMPF with an adaptive particle number strategy is proposed. The sampling number in each sub-filter of the IMMPF algorithm is adaptively changed, according to the value of the mixing probability. When the mixing probability exceeds the designed threshold, which is about 5∼8 times of the initial mode transition probability, an appropriate strategy is designed by making a decision of the dominant model in the mode set. Then, the sampling number is increased in the dominant model and decreased in non-dominant models respectively. The simulation result shows that this method has a prior performance than the general IMMPF with a fixed particle number and a similar computational cost.

Original languageEnglish
Title of host publication2012 5th International Congress on Image and Signal Processing, CISP 2012
Pages1480-1484
Number of pages5
DOIs
StatePublished - 2012
Event2012 5th International Congress on Image and Signal Processing, CISP 2012 - Chongqing, China
Duration: 16 Oct 201218 Oct 2012

Publication series

Name2012 5th International Congress on Image and Signal Processing, CISP 2012

Conference

Conference2012 5th International Congress on Image and Signal Processing, CISP 2012
Country/TerritoryChina
CityChongqing
Period16/10/1218/10/12

Keywords

  • IMM
  • mixing probability
  • multi-model
  • PF
  • sample number

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