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Finite dimensional algorithms for optimal scheduling of hidden Markov model sensors
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-1927-1690
2001 (English)In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Salt Lake, UT, 2001, Vol. 6, 3973-3976 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. The problem of determining the optimal measurement policy is solved via stochastic dynamic programming. An optimal finite dimensional algorithm is presented along with numerical results.

Place, publisher, year, edition, pages
Salt Lake, UT, 2001. Vol. 6, 3973-3976 p.
, 2001 IEEE Interntional Conference on Acoustics, Speech, and Signal Processing
Keyword [en]
Algorithms, Dynamic programming, Estimation, Markov processes, Matrix algebra, Piecewise linear techniques, Radar systems, State space methods, Finite dimensional algorithms, Hidden markov model sensors, Optimal measurement scheduling policy, Sensor scheduling problem, Stochastic dynamic programming, Sensors
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-55407OAI: diva2:471627
Sponsors: IEEE 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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