An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints
Data(s) |
10/11/2011
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Resumo |
We consider a robust filtering problem for uncertain discrete-time, homogeneous, first-order, finite-state hidden Markov models (HMMs). The class of uncertain HMMs considered is described by a conditional relative entropy constraint on measures perturbed from a nominal regular conditional probability distribution given the previous posterior state distribution and the latest measurement. Under this class of perturbations, a robust infinite horizon filtering problem is first formulated as a constrained optimization problem before being transformed via variational results into an unconstrained optimization problem; the latter can be elegantly solved using a risk-sensitive information-state based filtering. |
Formato |
application/pdf |
Identificador | |
Relação |
http://eprints.qut.edu.au/46498/1/FLU.2.R.submit.pdf http://www.aucc.org.au/ Ford, Jason J., Ugrinovskii, Valery A., & Lai, John S. (2011) An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints. In Australian Control Conference, University of Melbourne, Melbourne, VIC. http://purl.org/au-research/grants/ARC/LP100100302 |
Direitos |
Copyright 2011 [please consult the authors] |
Fonte |
Australian Research Centre for Aerospace Automation; Faculty of Built Environment and Engineering; School of Engineering Systems |
Palavras-Chave | #090100 AEROSPACE ENGINEERING #090602 Control Systems Robotics and Automation #090609 Signal Processing #robust filtering #hidden Markov models (HMMs) #conditional relative entropy |
Tipo |
Conference Paper |