Truncated unscented particle filter for dealing with non-linear inequality constraints

Miao Yu, Wen Hua Chen, Jonathon Chambers

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

This paper addresses state estimation where domain knowledge is represented by non-linear inequality constraints. To cope with non-Gaussian state distribution caused by the utilisation of domain knowledge, a truncated unscented particle filter method is proposed in this paper. Different from other particle filtering schemes, a truncated unscented Kalman filter is adopted as the importance function for sampling new particles in the proposed truncated unscented particle scheme. Consequently more effective particles are generated and a better state estimation result is then obtained. The advantages of the proposed truncated unscented particle filter algorithm over the state-of-the-art particle filters are demonstrated through Monte-Carlo simulations.

Original languageEnglish
Title of host publication2014 Sensor Signal Processing for Defence, SSPD 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479952946
DOIs
Publication statusPublished - 31 Oct 2014
Event4th Conference of the Sensor Signal Processing for Defence, SSPD 2014 - Edinburgh, United Kingdom
Duration: 8 Sept 20149 Sept 2014

Publication series

Name2014 Sensor Signal Processing for Defence, SSPD 2014

Conference

Conference4th Conference of the Sensor Signal Processing for Defence, SSPD 2014
Country/TerritoryUnited Kingdom
CityEdinburgh
Period8/09/149/09/14

ASJC Scopus subject areas

  • Signal Processing
  • Computer Science Applications
  • Electrical and Electronic Engineering

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