Hidden Markov model state estimation with randomly delayed observations

TitleHidden Markov model state estimation with randomly delayed observations
Publication TypeJournal Article
Year of Publication1999
AuthorsEvans, J. S., and V. Krishnamurthy
JournalSignal Processing, IEEE Transactions on
Pagination2157 -2166
Date Publishedaug.
Keywordsaugmented state HMM, connectionless packet switched communications network, delays, discrete-time hidden Markov model, distributed sensors, filtering, filtering theory, finite state Markov chain, hidden Markov model state estimation, hidden Markov models, measurements transmission, packet switching, performance, random processes, randomly delayed observations, recursive filter, recursive filters, simulations, state estimation, state estimation algorithms, telecommunication networks

This paper considers state estimation for a discrete-time hidden Markov model (HMM) when the observations are delayed by a random time. The delay process is itself modeled as a finite state Markov chain that allows an augmented state HMM to model the overall system. State estimation algorithms for the resulting HMM are then presented, and their performance is studied in simulations. The motivation for the model stems from the situation when distributed sensors transmit measurements over a connectionless packet switched communications network


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