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Proceedings Paper

Confirmation sensor scheduling using a reinforcement learning approach
Author(s): Jay A. Marble; Doron Blatt; Alfred O. Hero
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Paper Abstract

Landmine sensor technology research has proposed many types of sensors. Some of this technology has matured and can be implemented in sensor arrays that scan for landmines. Other technologies show great promise for distinguishing landmines from clutter, but are more practical to implement on a point-by-point basis as confirmation sensors. This work looks at the problem of scheduling confirmation sensors. Three sensors are considered for their ability to distinguish between landmines and clutter. A novel sensor scheduling algorithm is employed that learns an optimal policy for applying confirmation sensors based on reinforcement learning. A performance gain is realized in both probability of correct classification and processing time. The processing time savings come from not having to deploy all sensors for every situation.

Paper Details

Date Published: 18 May 2006
PDF: 6 pages
Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 62172V (18 May 2006); doi: 10.1117/12.665900
Show Author Affiliations
Jay A. Marble, Univ. of Michigan (United States)
Doron Blatt, Univ. of Michigan (United States)
Alfred O. Hero, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 6217:
Detection and Remediation Technologies for Mines and Minelike Targets XI
J. Thomas Broach; Russell S. Harmon; John H. Holloway Jr., Editor(s)

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