Nonmyopic multiaspect sensing with partially observable Markov decision processes

Published

Journal Article

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. © 2007 IEEE.

Full Text

Duke Authors

Cited Authors

  • Ji, S; Parr, R; Carin, L

Published Date

  • June 1, 2007

Published In

Volume / Issue

  • 55 / 6 I

Start / End Page

  • 2720 - 2730

International Standard Serial Number (ISSN)

  • 1053-587X

Digital Object Identifier (DOI)

  • 10.1109/TSP.2007.893747

Citation Source

  • Scopus