Adaptive multimodality sensing of landmines


Journal Article

The problem of adaptive multimodality sensing of landmines is considered based on electromagnetic induction (EMI) and ground-penetrating radar (GPR) sensors. Two formulations are considered based on a partially observable Markov decision process (POMDP) framework. In the first formulation, it is assumed that sufficient training data are available, and a POMDP model is designed based on physics-based features, with model selection performed via a variational Bayes analysis of several possible models. In the second approach, the training data are assumed absent or insufficient, and a lifelong-learning approach is considered, in which exploration and exploitation are integrated. We provide a detailed description of both formulations, with example results presented using measured EMI and GPR data, for buried mines and clutter. © 2007 IEEE.

Full Text

Duke Authors

Cited Authors

  • He, L; Ji, S; Scott, WR; Carin, L

Published Date

  • June 1, 2007

Published In

Volume / Issue

  • 45 / 6

Start / End Page

  • 1756 - 1773

International Standard Serial Number (ISSN)

  • 0196-2892

Digital Object Identifier (DOI)

  • 10.1109/TGRS.2007.894933

Citation Source

  • Scopus