Unexploded ordnance detection using Bayesian physics-based data fusion

Detection of unexploded ordnance (UXO) represents a major challenge on closed, closing, and transferred military ranges as well as on active installations. On sites contaminated with UXO, extensive surface and sub-surface clutter is also present. Traditional methods used for UXO remediation have severe 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. In this paper, sensor fusion techniques are employed using field data from magnetometer and electromagnetic induction (EMI) sensors in order to mitigate false alarms. Rigorous sensor response models are developed based on the sensor physics for both a traditional time-domain EMI sensor and a recently developed wideband frequency-domain sensor. Features of the target signatures are extracted by inverting the measured sensor data associated with an anomaly using the physical model. The statistical uncertainty in the feature space is explicitly treated using a Bayesian processor to discriminate targets from clutter. Discrimination performance on a seeded field trial conducted previously is reviewed. Performance on a recent field trial where data was collected in a more realistic survey mode is then presented, illustrating the robustness of the approach. Substantial reduction of the false alarm rate is achieved.