NDLI: Rollout strategy for Hidden Markov Model (HMM)-based dynamic sensor scheduling

Content Provider	IEEE Xplore Digital Library
Author	Hyunsung Lee Singh, S. Woosun An Gokhale, S.S. Pattipati, K. Kleinman, D.L.
Copyright Year	2007
Description	Author affiliation: Univ. of Connecticut, Storrs (Hyunsung Lee; Singh, S.; Woosun An)
Abstract	In this paper, a hidden Markov model (HMM)-based dynamic sensor scheduling problem is formulated, and solved using rollout concepts to overcome the computational intractability of the dynamic programming (DP) recursion. The problem considered here involves dynamically sequencing a set of sensors to minimize the sum of sensor cost and the HMM state estimation error cost. The surveillance task is modeled as a single HMM with multiple emission matrices corresponding to each of the sensors. The rollout information gain (RIG) algorithm proposed herein employs the information gain (IG) heuristic as the base algorithm. The RIG algorithm is illustrated on an intelligence, surveillance, and reconnaissance (ISR) scenario of a village for the presence of weapons and terrorists/refugees. Extension of the RIG strategy to monitor multiple HMMs involves combining the information gain heuristic with the auction algorithm that computes the K -best assignments at each decision epoch of rollout.
Starting Page	553
Ending Page	558
File Size	516913
Page Count	6
File Format	PDF
ISBN	9781424409907
DOI	10.1109/ICSMC.2007.4414200
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Dynamic scheduling Processor scheduling Dynamic programming Costs Surveillance Intelligent sensors State estimation Reconnaissance Weapons
Content Type	Text
Resource Type	Article

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