Filtering and smoothing state estimation for flag Hidden Markov Models

State detection is studied for a special class of flag Hidden Markov Models (HMMs), which comprise 1) an arbitrary finite-state underlying Markov chain and 2) a structured observation process wherein a subset of states emit distinct flags while other states are unmeasured. For flag HMMs, an explicit...

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Bibliographic Details
Published in2016 American Control Conference (ACC) pp. 7042 - 7047
Main Authors Doty, Kyle, Roy, Sandip, Fischer, Thomas R.
Format Conference Proceeding Journal Article
LanguageEnglish
Published American Automatic Control Council (AACC) 01.07.2016
Subjects
Conferences
Electronics
Error analysis
Error detection
Error probability
Filtering
Filtration
Flags
Hidden Markov models
Markov processes
Mathematical models
Maximum likelihood detection
Maximum likelihood estimation
Smoothing methods
State estimation
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Summary:State detection is studied for a special class of flag Hidden Markov Models (HMMs), which comprise 1) an arbitrary finite-state underlying Markov chain and 2) a structured observation process wherein a subset of states emit distinct flags while other states are unmeasured. For flag HMMs, an explicit computation of the probability of error for the maximum-likelihood smoother is developed. Also, some structural results are obtained for maximum likelihood detectors and their error probabilities. These algebraic and structural results are leveraged to address sensor placement in three examples, including one on activity-monitoring in a home environment that is drawn from field data.
Bibliography:ObjectType-Article-2
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SourceType-Conference Papers & Proceedings-2
ISSN:2378-5861
DOI:10.1109/ACC.2016.7526783