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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Angelova, D., & Mihaylova, L. (2006). A Monte Carlo algorithm for state and parameter estimation of extended targets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3993 LNCS-III, pp. 624–631). Springer Verlag. https://doi.org/10.1007/11758532_82
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