Application of the theory of optimal experiments to adaptive electromagnetic-induction sensing of buried targets

Xuejun Liao, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

A mobile electromagnetic-induction (EMI) sensor is considered for detection and characterization of buried conducting and/or ferrous targets. The sensor may be placed on a robot and, here, we consider design of an optimal adaptive-search strategy. A frequency-dependent magnetic-dipole model is used to characterize the target at EMI frequencies. The goal of the search is accurate characterization of the dipole-model parameters, denoted by the vector Θ; the target position and orientation are a subset of Θ. The sensor position and operating frequency are denoted by the parameter vector p and a measurement is represented by the pair (p, O), where O denotes the observed data. The parameters p are fixed for a given measurement, but, in the context of a sequence of measurements p may be changed adaptively. In a locally optimal sequence of measurements, we desire the optimal sensor parameters, PN+1 for estimation of Θ, based on the previous measurements (pn, On n=1,N. The search strategy is based on the theory of optimal experiments, as discussed in detail and demonstrated via several numerical examples. © 2004 IEEE.
Original languageEnglish (US)
Pages (from-to)961-972
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume26
Issue number8
DOIs
StatePublished - Aug 1 2004
Externally publishedYes

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