Model-based statistical sensor fusion for unexploded ordnance detection
Detection and remediation of unexploded ordnance (UXO) represents a major challenge on closed, closing, and transferred military ranges as well as on active installations. The detection problem is exacerbated by the fact that on sites contaminated with UXO, extensive surface and sub-surface clutter and shrapnel is also present. Traditional methods used for UXO remediation have difficulty distinguishing buried UXO from these anthropic clutter items as well as from naturally occurring magnetic geologic noise, and thus incur prohibitively high false alarm rates. The reduction of the false alarm rate has proven to be the greatest challenge for UXO remediation. In this paper, sensor fusion techniques are applied to field data from magnetometer and electromagnetic induction (EMI) sensors in order to determine to what degree such an approach results in false alarm mitigation. The adoption of a model consisting of multiple non co-located dipoles is shown to improve our ability to predict measured signatures. A Monte Carlo fitting procedure in which multiple initial conditions is utilized for the inversion process. The statistical uncertainty in the feature space is explicitly treated using a Bayesian processor to discriminate targets from clutter. Substantial reduction of the false alarm rate is achieved for a recently developed frequency-domain EMI system. Furthermore, we investigate the effects of the processing bandwidth on discrimination performance for the frequency-domain system. The results indicate that performance can be improved by limiting the processing bandwidth to those frequencies that are the most robust to naturally occurring geological noise.
