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Derivation of the PHD filter based on direct Kullback-Leibler divergence minimisation

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Authors
García-Fernández, Ángel
Vo, Ba-Ngu
Date
2015
Type
Conference Paper

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García-Fernández, Á. and Vo, B. 2015. Derivation of the PHD filter based on direct Kullback-Leibler divergence minimisation, in Proceedings of the 2015 International Conference on Control, Automation and Information Sciences (ICCAIS), Oct 29-31 2015, pp. 209-213. Changshu: IEEE.
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ICCAIS 2015 - 4th International Conference on Control, Automation and Information Sciences
Source Conference
2015 International Conference on Control, Automation and Information Sciences
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Department of Electrical and Computer Engineering
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Abstract

In this paper, we provide a novel derivation of the probability hypothesis density (PHD) filter without using probability generating functionals or functional derivatives. The PHD filter fits in the context of assumed density filtering and implicitly performs Kullback-Leibler divergence (KLD) minimisations after the prediction and update steps. The novelty of this paper is that the KLD minimisation is performed directly on the multitarget prediction and posterior densities.