Partially Observable Markov Decision Process Approximations for Adaptive Sensing
Discrete Event Dynamic Systems
An Information Roadmap Method for Robotic Sensor Path Planning
Journal of Intelligent and Robotic Systems
Optimal value of information in graphical models
Journal of Artificial Intelligence Research
Optimal threshold policies for multivariate POMDPs in radar resource management
IEEE Transactions on Signal Processing
Information-driven search strategies in the board game of CLUE®
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information-driven sensor path planning by approximate cell decomposition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Submodularity and its applications in optimized information gathering
ACM Transactions on Intelligent Systems and Technology (TIST)
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We consider the problem of sensing a concealed or distant target by interrogation from multiple sensors situated on a single platform. The available actions that may be taken are selection of the next relative target-platform orientation and the next sensor to be deployed. The target is modeled in terms of a set of states, each state representing a contiguous set of target-sensor orientations over which the scattering physics is relatively stationary. The sequence of states sampled at multiple target-sensor orientations may be modeled as a Markov process. The sensor only has access to the scattered fields, without knowledge of the particular state being sampled, and, therefore, the problem is modeled as a partially observable Markov decision process (POMDP). The POMDP yields a policy, in which the belief state at any point is mapped to a corresponding action. The nonmyopic policy is compared to an approximate myopic approach, with example results presented for measured underwater acoustic scattering data