Approximate dynamic programming for sensor management

This paper studies the problem of dynamic scheduling of multi-mode sensor resources for the problem of classification of multiple unknown objects. Because of the uncertain nature of the object types, the problem is formulated as a partially observed Markov decision problem with a large state space....

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Bibliographic Details
Published inProceedings of the 36th IEEE Conference on Decision and Control Vol. 2; pp. 1202 - 1207 vol.2
Main Author Castanon, D.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Dynamic programming
Dynamic scheduling
Job shop scheduling
Lagrangian functions
Samarium
Sensor phenomena and characterization
Sensor systems and applications
State-space methods
Stochastic processes
Testing
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Summary:This paper studies the problem of dynamic scheduling of multi-mode sensor resources for the problem of classification of multiple unknown objects. Because of the uncertain nature of the object types, the problem is formulated as a partially observed Markov decision problem with a large state space. The paper describes a hierarchical algorithm approach for efficient solution of sensor scheduling problems with large numbers of objects, based on a combination of stochastic dynamic programming and nondifferentiable optimization techniques. The algorithm is illustrated with an application involving classification of 10,000 unknown objects.
ISBN:0780341872
9780780341876
ISSN:0191-2216
DOI:10.1109/CDC.1997.657615