Modified unscented particle filter using variance reduction factor

E. Baser, I. Bilik

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

4 Scopus citations

Abstract

Sequential Monte Carlo based estimators, also known as particle filters (PF), have been widely used in nonlinear and non-Gaussian estimation problems. However, efficient distribution of the limited number of random samples remains a critical issue in design of the sequential Monte Carlo based estimation algorithms. In this work, we derive a modified unscented particle filter based on variance reduction factor that obtains an efficient distribution of the random samples using a scaled unscented transform. The proposed algorithm is shown to combine the robustness of the unscented particle filter with relatively low computational complexity of the generic particle filter. The efficiency of the proposed approach is evaluated in nonlinear problem of bearings-only target tracking, and its performance is compared to the regularized PF and the Cramer-Rao low bound.

Original languageEnglish
Title of host publication2010 IEEE Radar Conference
Subtitle of host publicationGlobal Innovation in Radar, RADAR 2010 - Proceedings
Pages893-898
Number of pages6
DOIs
StatePublished - 30 Jul 2010
Externally publishedYes
EventIEEE International Radar Conference 2010, RADAR 2010 - Washington DC, United States
Duration: 10 May 201014 May 2010

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659

Conference

ConferenceIEEE International Radar Conference 2010, RADAR 2010
Country/TerritoryUnited States
CityWashington DC
Period10/05/1014/05/10

Fingerprint

Dive into the research topics of 'Modified unscented particle filter using variance reduction factor'. Together they form a unique fingerprint.

Cite this