Adaptive multiview target classification in synthetic aperture sonar images using a partially observable markov decision process

Vincent Myers, David P. Williams

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

The problem of classifying targets in sonar images from multiple views is modeled as a partially observable Markov decision process (POMDP). This model allows one to adaptively determine which additional views of an object would be most beneficial in reducing the classification uncertainty. Acquiring these additional views is made possible by employing an autonomous underwater vehicle (AUV) equipped with a side-looking imaging sonar. The components of the multiview target classification POMDP are specified. The observation model for a target is specified by the degree of similarity between the image under consideration and a number of precomputed templates. The POMDP is validated using real synthetic aperture sonar (SAS) data gathered during experiments at sea carried out by the NATO Undersea Research Centre, and results show that the accuracy of the proposed method outperforms an approach using a number of predetermined view aspects. The approach provides an elegant way to fully exploit multiview information and AUV maneuverability in a methodical manner.

Original languageEnglish (US)
Article number6104192
Pages (from-to)45-55
Number of pages11
JournalIEEE Journal of Oceanic Engineering
Volume37
Issue number1
DOIs
StatePublished - Jan 2012

All Science Journal Classification (ASJC) codes

  • Ocean Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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