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Algorithms for scheduling of hidden Markov model sensors
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-1927-1690
2001 (English)In: Proceedings of the IEEE Conference on Decision and Control, Orlando, FL, 2001, Vol. 5, 4818-4819 p.Conference paper (Refereed)
Abstract [en]

Consider the Hidden Markov model estimation problem 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 estimation errors and measurement costs.

Place, publisher, year, edition, pages
Orlando, FL, 2001. Vol. 5, 4818-4819 p.
, 40th IEEE Conference on Decision and Control (CDC)
Keyword [en]
Algorithms, Constraint theory, Markov processes, Mathematical models, Matrix algebra, Scheduling, State space methods, Vectors, Estimation errors, Hidden Markov model sensors, Measurement costs, Sensors
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-55405OAI: diva2:471631
References: Athans, M., On the determination of optimal costly measurement strategies for linear stochastic systems (1972) Automatica, 8, pp. 397-412; Bertsekas, D.P., (1995) Dynamic Programming and Optimal Control, 1-2. , Athena Scientific, Belmont, Massachusetts; Cassandra, A.R., (1998) Exact and Approximate Algorithms for Partially Observed Markov Decision Process, , PhD thesis, Brown University; Evans, J.S., Krishnamurthy, V., Optimal sensor scheduling for hidden Markov models (1998) IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP98, pp. 2161-2164. , Seattle, May; to appear International Journal of Control, Special Issue on Parameter Identification and State Estimation for Nonlinear Models, 2001; Lovejoy, W.S., A survey of algorithmic methods for partially observed Markov decision processes (1991) Annals of Operations Research, 28, pp. 47-66; Meier, L., Perschon, J., Dressler, R.M., Optimal control of measurement systems (1967) IEEE Transactions on Automatic Control, 12 (5), pp. 528-536. , October; Smallwood, R.D., Sondik, E.J., Optimal control of partially observable Markov processes over a finite horizon (1973) Operations Research, 21, pp. 1071-1088 NR 20140805Available from: 2012-01-02 Created: 2012-01-02 Last updated: 2013-09-05Bibliographically approved

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ReferencesLink to record
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