Finite dimensional algorithms for optimal scheduling of hidden Markov model sensors

Sponsors: IEEE NR 20140805
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.

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Reglerteknik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering hsv//eng
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

Wahlberg, Bo,1959-KTH,Signaler, sensorer och system(Swepub:kth)u18c1tfa (författare)
KTHSignaler, sensorer och system (creator_code:org_t)

Ingår i:ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - ProceedingsSalt Lake, UT, s. 3973-3976

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