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Application of the Theory of Optimal Experiments to Adaptive Electromagnetic-Induction Sensing of Buried Targets
August 2004 (vol. 26 no. 8)
pp. 961-972

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

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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 and Machine Intelligence, vol. 26, no. 8, pp. 961-972, Aug. 2004, doi:10.1109/TPAMI.2004.38
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