Nonmyopic multiaspect sensing with partially observable Markov decision processes

Shihao Ji, Ronald Parr, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

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. © 2007 IEEE.
Original languageEnglish (US)
Pages (from-to)2720-2730
Number of pages11
JournalIEEE Transactions on Signal Processing
Volume55
Issue number6 I
DOIs
StatePublished - Jun 1 2007
Externally publishedYes

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