Skip to main navigation Skip to search Skip to main content

Joint estimation of states and transition functions of dynamic systems using cost-reference particle filtering

  • University of A Coruna
  • Stony Brook University

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

1 Scopus citations

Abstract

The recently introduced cost-reference particle filter (CRPF) methodology allows for recursive estimation of unobserved states of dynamical systems without a priori knowledge of probability distributions of the noises in the system. In this paper, we use CRPFs in problems where we eliminate one more strong assumption about the state space model, the one of knowing the function governing the state evolution. We replace this function by a linearly combined set of basis functions where the linear combination coefficients are unknown. We show how CRPFs can be modified to cope with this scenario and demonstrate their performance for positioning a moving vehicle in a two-dimensional space.

Original languageEnglish
Title of host publication2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Signal Proces. Education, Spec. Sessions
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesIV361-IV364
ISBN (Print)0780388747, 9780780388741
DOIs
StatePublished - 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: Mar 18 2005Mar 23 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
VolumeIV
ISSN (Print)1520-6149

Conference

Conference2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Country/TerritoryUnited States
CityPhiladelphia, PA
Period03/18/0503/23/05

Fingerprint

Dive into the research topics of 'Joint estimation of states and transition functions of dynamic systems using cost-reference particle filtering'. Together they form a unique fingerprint.

Cite this