Skip to main navigation Skip to search Skip to main content

Unscented particle filter with estimation windows in submarine tracking

  • Shenmin Song*
  • , Xiqing Wei
  • , Peng Li
  • , Baoqun Zhang
  • *Corresponding author for this work
  • School of Astronautics, Harbin Institute of Technology

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

Abstract

In order to estimate the state of uncertain models, a robust filter based on risk sensitive estimator is proposed, which could automatically change the state noise covariance according to the magnitude of the risk function. As a result, sample impoverishment could be mitigated. Another contribution of this paper is to take every sensor measurement into account, when large sample sets are needed to represent the system's uncertainty, thereby avoiding the risk of losing valuable sensor information during the update of the filter. A simulation example of submarine bearing and frequency tracking is presented, the experiment results show that new algorithm performs better than generic particle filter and unscented particle filter.

Original languageEnglish
Title of host publication2010 8th World Congress on Intelligent Control and Automation, WCICA 2010
Pages137-140
Number of pages4
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 8th World Congress on Intelligent Control and Automation, WCICA 2010 - Jinan, China
Duration: 7 Jul 20109 Jul 2010

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)

Conference

Conference2010 8th World Congress on Intelligent Control and Automation, WCICA 2010
Country/TerritoryChina
CityJinan
Period7/07/109/07/10

Keywords

  • Estimation windows
  • Robust estimator
  • Unscented particle filter

Fingerprint

Dive into the research topics of 'Unscented particle filter with estimation windows in submarine tracking'. Together they form a unique fingerprint.

Cite this