State Estimation Algorithms for Markov Chains Observed in Arbitrary Noise
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 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]
|Collections||ANU Research Publications|
|Source:||Proceedings of IEEE Conference on Decision and Control 2008|
|01_Malcolm_State_Estimation_Algorithms_2008.pdf||190.3 kB||Adobe PDF||Request a copy|
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