Algorithms for optimal scheduling and management of hidden Markov model sensors

TitleAlgorithms for optimal scheduling and management of hidden Markov model sensors
Publication TypeJournal Article
Year of Publication2002
AuthorsKrishnamurthy, V.
JournalSignal Processing, IEEE Transactions on
Pagination1382 -1397
Date Publishedjun.
Keywordsaircraft, aircraft identification, array signal processing, communication constraints, computational constraints, cost function, dynamic programming, estimation errors, hidden Markov model sensors management, hidden Markov models, HMM sensors management, identification, Markov chain, measurement cost, measurement costs, noisy sensors, optimal measurement scheduling policy, optimal scheduling algorithms, optimisation, sensor scheduling, signal processing, stochastic dynamic programming, stochastic programming, suboptimal scheduling algorithms

The author considers a hidden Markov model (HMM) where a single Markov chain is observed by a number of noisy sensors. Due to computational or communication constraints, at each time instant, one can select only one of the noisy sensors. The sensor scheduling problem involves designing algorithms for choosing dynamically at each time instant which sensor to select to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy to minimize a cost function of estimation errors and measurement costs. The optimal measurement policy is solved via stochastic dynamic programming. Sensor management issues and suboptimal scheduling algorithms are also presented. A numerical example that deals with the aircraft identification problem is presented


a place of mind, The University of British Columbia

Electrical and Computer Engineering
2332 Main Mall
Vancouver, BC Canada V6T 1Z4
Tel +1.604.822.2872
Fax +1.604.822.5949

Emergency Procedures | Accessibility | Contact UBC | © Copyright 2021 The University of British Columbia