Optimal sensor scheduling for Hidden Markov models

TitleOptimal sensor scheduling for Hidden Markov models
Publication TypeConference Paper
Year of Publication1998
AuthorsEvans, J., and V. Krishnamurthy
Conference NameAcoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Pagination2161 -2164 vol.4
Date Publishedmay.
Keywordsarray signal processing, cost function minimisation, direction-of-arrival estimation, dynamic programming, estimation errors, hidden Markov models, Markov chain, measurement, measurement costs, noise, noisy sensors, optimal algorithm, optimal measurement scheduling policy, optimal sensor scheduling, scheduling, sensor model, signal model, signal processing, stochastic dynamic programming, stochastic programming, time instant selection

Consider the Hidden Markov model where the realization of a single Markov chain is observed by a number of noisy sensors. The sensor scheduling problem for the resulting Hidden Markov model is as follows: design an optimal algorithm for selecting at each time instant, one of the many sensors to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy, so as to minimize a cost function of the estimation errors and measurement costs. The problem of determining the optimal measurement policy is solved via stochastic dynamic programming. Numerical results are presented


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