Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes
Shihao Ji
Parr, R.
Carin, L.
Dept. of Electr. & Comput. Eng, Duke Univ., Durham, NC;
This paper appears in: Signal Processing, IEEE Transactions on
Publication Date: June 2007
Volume: 55,
Issue: 6, Part 1
On page(s): 2720-2730
ISSN: 1053-587X
INSPEC Accession Number: 9472284
Digital Object Identifier: 10.1109/TSP.2007.893747
Current Version Published: 2007-05-21
Abstract
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
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