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State Estimation Algorithms for Markov Chains Observed in Arbitrary Noise

Malcolm, William


In this article we compute state estimation schemes for discrete-time Markov chains observed in arbitrary observation noise. Here we assume the observation noise distribution is known in advance. Appealing to a fundamental L1 convergence result in[1] we propose to represent any practical observation noise model by a convex combination of Gaussian densities, that is, a mixture function that is itself a valid probability density function. To compute our state estimation schemes we use the...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Conference paper
Source: Proceedings of IEEE Conference on Decision and Control 2008
DOI: 10.1109/CDC.2008.4738600


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