Adaptive multimodality sensing of landmines

Lihan He, Shihao Ji, Waymond R. Scott, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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.
Original languageEnglish (US)
Pages (from-to)1756-1773
Number of pages18
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume45
Issue number6
DOIs
StatePublished - Jun 1 2007
Externally publishedYes

Fingerprint Dive into the research topics of 'Adaptive multimodality sensing of landmines'. Together they form a unique fingerprint.

Cite this