CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2004 vol.26 Issue No.08 - August

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Issue No.08 - August (2004 vol.26)

pp: 961-972

Lawrence Carin , IEEE

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.38

ABSTRACT

<p><b>Abstract</b>—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 <tmath>{\Theta}</tmath>; the target position and orientation are a subset of <tmath>\Theta</tmath>. The sensor position and operating frequency are denoted by the parameter vector <tmath>{\schmi{p}}</tmath> and a measurement is represented by the pair <tmath>({\schmi{p,O}})</tmath>, where <tmath>{\schmi{O}}</tmath> denotes the observed data. The parameters <tmath>{\schmi{p}}</tmath> are fixed for a given measurement, but, in the context of a sequence of measurements <tmath>{\schmi{p}}</tmath> may be changed adaptively. In a locally optimal sequence of measurements, we desire the optimal sensor parameters, <tmath>{\schmi{p}}_{N+1}</tmath> for estimation of <tmath>\Theta</tmath>, based on the previous measurements <tmath>({\schmi{p}}_n,{\schmi{\schmi{O}}}_n)_{n=1,N}</tmath>. The search strategy is based on the theory of optimal experiments, as discussed in detail and demonstrated via several numerical examples.</p>

INDEX TERMS

Optimal experiment, sensing, adaptive processing.

CITATION

Xuejun Liao, Lawrence Carin, "Application of the Theory of Optimal Experiments to Adaptive Electromagnetic-Induction Sensing of Buried Targets",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.26, no. 8, pp. 961-972, August 2004, doi:10.1109/TPAMI.2004.38