An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints
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.
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.
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|Item Type:||Conference Paper|
|Keywords:||robust filtering, hidden Markov models (HMMs), conditional relative entropy|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
|Divisions:||Current > Research Centres > Australian Research Centre for Aerospace Automation
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2011 [please consult the authors]|
|Deposited On:||16 Oct 2011 22:02|
|Last Modified:||10 Dec 2015 08:38|
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