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<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>
Optimal experiment, sensing, adaptive processing.

L. Carin and X. Liao, "Application of the Theory of Optimal Experiments to Adaptive Electromagnetic-Induction Sensing of Buried Targets," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. , pp. 961-972, 2004.
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