Mixture truncated unscented Kalman filtering

García-Fernández, AF, Morelande, MR and Grajal, J (2012) Mixture truncated unscented Kalman filtering .

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Abstract

This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to each component of the modified prior mixture can improve the approximation to the posterior provided by the Kalman filter. In practice, bounded support is not necessary. © 2012 ISIF (Intl Society of Information Fusi).

Item Type:	Conference Item (Unspecified)
Divisions:	Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	19 Apr 2021 09:21
Last Modified:	05 Jun 2025 17:07
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3119491
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