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Various Topics on Angle-Only Tracking using Particle Filters
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2002 (English)Report (Other academic)
Abstract [en]

Angle-only tracking estimates range and range rate from measured angle information by maneuvering the observation platform to gain observability. Traditionally, linear or linearized models are used, where the uncertainty in the sensor and motion models is typically modeled by Gaussian densities. Hence, classical sub-optimal Bayesian methods based on linearized Kalman filters can be used. The sequential Monte Carlo method, or particle filter, provides an approximative solution to the non-linear and non-Gaussian estimation problem. The particle filter approximates the optimal solution, hence it can outperform the Kalman filter in many cases, given sufficient computational resources. In an air-to-sea application it is shown how to incorporate terrain induced constraints using a terrain database. The algorithm is also successfully evaluated on experimental sonar data acquired from a torpedo system.

Place, publisher, year, edition, pages
Linköping: Linköping University , 2002. , 6 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2473
Keyword [en]
Target tracking, Particle filter, Bayesian estimation, Non-linearfiltering, Hard constraints, Angle-only Tracking
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55895ISRN: LiTH-ISY-R-2473OAI: oai:DiVA.org:liu-55895DiVA: diva2:316636
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-14Bibliographically approved

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Karlsson, Rickard

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf