This Article 
 Bibliographic References 
 Add to: 
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.

[1] L. Carin, H. Yu, Y. Dalichaouch, A.R. Perry, P.V. Czipott, and C.E. Baum, On the Wideband EMI Response of a Rotationally Symmetric Permeable and Conducting Target IEEE Trans. Geoscience Remote Sensing, vol. 39, pp. 1206-1213, June 2001.
[2] Y. Zhang, L. Collins, H. Yu, C. Baum, and L. Carin, Sensing of Unexploded Ordnance with Magnetometer and Induction Data: Theory and Signal Processing IEEE Trans. Geoscience Remote Sensing, vol. 41, pp. 1005-1015, May 2003.
[3] N. Geng, C.E. Baum, and L. Carin, On the Low-Frequency Natural Response of Conducting and Permeable Targets IEEE Trans. Geoscience Remote Sensing, vol. 37, pp. 347-359, Jan. 1999.
[4] S.M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Englewood Cliffs, N.J.: Prentice Hall, 1993.
[5] D.J.C. MacKay, Information-based Objective Functions for Active Data Selection Neural Computation, vol. 4, pp. 590-604, 1992.
[6] D.V. Lindley, On a Measure of the Information Provided by an Experiment Annals of Math Statistics, vol. 27, no. 4, pp. 986-1005, Dec. 1956.
[7] H. Chernoff, Sequential Analysis and Optimal Design. SIAM, 1972.
[8] V.V. Fedorov, Theory of Optimal Experiments. Academic Press, 1972.
[9] K. Kastella, Discrimination Gain to Optimize Detection and Classification IEEE Trans. Systems, Man, and Cybernetics Part A: System and Humans, vol. 27, pp. 112-116, Jan. 1997.
[10] A.A. Abdel-Samad and A.H. Tewfik, Search Strategies for Radar Target Localization Proc. 1999 Int'l Conf. Image Processing, vol. 3, pp. 862-866, Oct. 1999.
[11] D.A. Castanon, Optimal Search Strategies in Dynamic Hypothesis Testing IEEE Trans. Systems, Man, and Cybernetics, vol. 25, pp. 1130-1138, July 1995.
[12] P. Whaite and F.P. Ferrie, "Autonomous Exploration: Driven by Uncertainty," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 3, pp. 193-205, Mar. 1997.
[13] M.H. Wright and P.E. Gill, Practical Optimization. Academic Press, 1997.
[14] T.M. Cover and J.A. Thomas, Elements of Information Theory. New York: Wiley, 1991.
[15] J.A. Simmons, A View of the World through the Bat's Ear: The Formation of Acoustic Images in Echolocation Cognition, vol. 33, pp. 155-199, 1989.

Index Terms:
Optimal experiment, sensing, adaptive processing.
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
Usage of this product signifies your acceptance of the Terms of Use.