Application of the Theory of Optimal Experiments to Adaptive Electromagnetic-Induction Sensing of Buried Targets

Abstract—Amobile electromagnetic-induction (EMI) sensor is considered for detection and characterization of buried conducting and/or ferrous targets. The sensormay 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, denotedby the vector; the target positionandorientationare a subset of. The sensor position and operating frequency are denoted by the parameter vector p and a measurement is represented by the pair ðp;OÞ, where O denotes theobserveddata. Theparameters p are fixed for agivenmeasurement, but, in thecontext of a sequenceofmeasurements pmay bechangedadaptively. In a locally optimal sequenceofmeasurements,wedesire theoptimal sensor parameters,pNþ1 for estimationof, based on the previous measurements ðpn; OnÞn1;N. The search strategy is based on the theory of optimal experiments, as discussed in detail and demonstrated via several numerical examples. Index Terms—Optimal experiment, sensing, adaptive processing.