Multiple model cardinalized probability hypothesis density filter

Ramona Georgescu; Peter Willett

doi:10.1117/12.890953

16 September 2011 Multiple model cardinalized probability hypothesis density filter

Ramona Georgescu, Peter Willett

Proceedings Volume 8137, Signal and Data Processing of Small Targets 2011; 81370L (2011) https://doi.org/10.1117/12.890953
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

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.

Citation Download Citation

Ramona Georgescu and Peter Willett "Multiple model cardinalized probability hypothesis density filter", Proc. SPIE 8137, Signal and Data Processing of Small Targets 2011, 81370L (16 September 2011); https://doi.org/10.1117/12.890953

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