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A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date28/05/2006
Host publicationICCS'06: Proceedings of the 6th international conference on Computational Science
Pages624-631
Number of pages8
Original languageEnglish

Conference

Conference6th International Conference on Computational Science
CityReading
Period28/05/0631/05/06

Conference

Conference6th International Conference on Computational Science
CityReading
Period28/05/0631/05/06

Abstract

This paper considers the joint state and parameter estimation of extended targets. Both the target kinematic states, position and speed, are estimated with the target extent parameters. The developed algorithm is applied to a ship, whose shape is modelled by an ellipse. A Bayesian sampling algorithm with finite mixtures is proposed for the evaluation of the extent parameters whereas a suboptimal Bayesian interacting multiple model (IMM) filter estimates the kinematic parameters of the maneuvering ship. The algorithm performance is evaluated by Monte Carlo comparison with a particle filtering approach.

Bibliographic note

V.N. Alexandrov et al. (Eds.), ICCS 2006, part III, LNCS Proceedings 3993, pp. 624-631, Springer-Verlag Berlin Heidelberg, May 28-31, 2006. ISBN: 978-3-540-34383-7 DOI: 10.1007/11758532_82