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Proceedings Paper

Multiple model cardinalized probability hypothesis density filter
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Paper Abstract

The Probability Hypothesis Density (PHD) filter propagates the first-moment approximation to the multi-target Bayesian posterior distribution while the Cardinalized PHD (CPHD) filter propagates both the posterior likelihood of (an unlabeled) target state and the posterior probability mass function of the number of targets. Extensions of the PHD filter to the multiple model (MM) framework have been published and were implemented either with a Sequential Monte Carlo or a Gaussian Mixture approach. In this work, we introduce the multiple model version of the more elaborate CPHD filter. We present the derivation of the prediction and update steps of the MMCPHD particularized for the case of two target motion models and proceed to show that in the case of a single model, the new MMCPHD equations reduce to the original CPHD equations.

Paper Details

Date Published: 17 September 2011
PDF: 10 pages
Proc. SPIE 8137, Signal and Data Processing of Small Targets 2011, 81370L (17 September 2011); doi: 10.1117/12.890953
Show Author Affiliations
Ramona Georgescu, Univ. of Connecticut (United States)
Peter Willett, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 8137:
Signal and Data Processing of Small Targets 2011
Oliver E. Drummond, Editor(s)

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