On the convexity of policy regions in partially observed systems
Operations Research
Some monotonicity results for partially observed Markov decision processes
Operations Research
A survey of algorithmic methods for partially observed Markov decision processes
Annals of Operations Research
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Partially Observed Markov Decision Process Multiarmed Bandits---Structural Results
Mathematics of Operations Research
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Networked sensor management and data rate control for tracking maneuvering targets
IEEE Transactions on Signal Processing
Algorithms for optimal scheduling and management of hidden Markovmodel sensors
IEEE Transactions on Signal Processing
Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes
IEEE Transactions on Signal Processing
Brief Optimizing the receiver maneuvers for bearings-only tracking
Automatica (Journal of IFAC)
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This paper deals with the management of multimode sensors such as multifunction radars. We consider the problems of multitarget radar scheduling formulated as multivariate partially observed Markov decision process (POMDPs). The aim is to compute the scheduling policy to determine which target to choose and how long to continue with this choice so as to minimize a cost function. We give sufficient conditions on the cost function, dynamics of the Markov chain target and observation probabilities so that the optimal scheduling policy has a threshold structure with respect to the multivariate TP2 ordering. This implies that the optimal parameterized policy can be estimated efficiently. We then present stochastic approximation algorithms for estimating the best multilinear threshold policy.