Study of Inverse Problems for Buried UXO Discrimination Based on EMI Sensor Data

摘要

The recently developed physics-based "mean field" formalism for efficiently computing the time-domain response of compact metallic targets is applied to the solution of model inverse problems for remote classification of buried UXO-like targets. The formalism is first used to compute model forward scattering data, in the form of time-domain decay curves as measured by EMI or magnetic field, for a sequence of canonical ellipsoidal target shapes of various geometries. This data is subsequently used as input to a genetic algorithm-based inversion routine, in which the target parameter model space, comprised of target shape, conductivity, location, orientation, etc., is efficiently searched to find the best fit to the data. Global search procedures, such as genetic algorithms, typically require the forward scattering solution for hundreds, or perhaps thousands, of candidate target models. To be practical, these forward solutions must be rapidly computable. Our solution approach has been specifically designed to meet this requirement. Of special interest is the ability of the inversion algorithm to distinguish robustly between UXO-like targets, modelled here as cylindrically shaped prolate spheroids, and, say, flat sheet-like clutter targets, modelled as very thin oblate spheroids.