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Abstract

In this thesis I investigate methods for discriminating between unexploded ordnances (UXOs) and clutter items (e.g: shrapnel, geology). I first describe a numerical forward model, the method of auxiliary sources (MAS), which can be used to model the magnetic and electromagnetic response of a conductive, permeable body. I use this model to validate the connection between the parameters of approximate forward models and target properties (i.e target shape). I also examine how model parameters can be estimated from observed data using inversion. I then describe algorithms for discriminating between UXO and clutter. In the statistical classification framework, model parameters are basis vectors within a multi-dimensional feature space. I prioritize features based upon their ability to separate U X O and clutter using canonical analysis. I describe two approaches for partitioning the feature space: modelling the underlying distributions from which the observed feature data are drawn, or directly defining a decision boundary. A suite of statistical classifiers are then applied to magnetics data acquired at three field sites. Finally, I propose an algorithm for selecting a classifier as target excavation proceeds.