Application of the theory of optimal experiments to adaptive electromagnetic-induction sensing of buried targets.
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 bythe vector theta; the target position and orientation are a subset of theta. 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 parametersp 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, P(N+1) for estimation of theta, based on the previous measurements (p(n), 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.
Liao
Xuejun
X
Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708-0291, USA. xjliao@ee.duke.edu
Carin
Lawrence
L
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D004574:Electromagnetic Fields; D005246:Feedback; D016247:Information Storage and Retrieval / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012987:Soil / Q000032:analysis*; D014159:Transducers
D012987:Soil