Improved UXO detection via sensor fusion

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Improved UXO detection via sensor fusion

Zhang, Yan / Jing Li / Carin, L. / Collins, L.M.

Traditional algorithms for UXO remediation experience severe difficulties distinguishing buried targets from anthropic clutter, and in most cases UXO items are found among extensive surface clutter and shrapnel from ordnance operations. These problems render site mediation a very slow, labor intensive, and inefficient process. While sensors have improved significantly in their ability to detect conducting and/or permeable targets, reduction of the false alarm rate has proven to be a significantly more challenging problem. Our work has focused on the development of optimal signal processing algorithms that rigorously incorporate the underlying physics characteristics of the sensor and the anticipated UXO target in order to address the false alarm issue. We describe several techniques for discriminating targets from clutter that have been applied to data obtained with the Multi-sensor Towed Array Detection System (MTADS). MTADS includes both EMI and magnetometer sensors. We describe a variety of signal processing techniques which incorporate physics-based models that have been applied to the data measured by MTADS during field demonstrations. We compare and contrast the performance of the various algorithms as well as discussing tradeoffs, such as training requirements. The results of this analysis quantify the utility of fusing magnetometer and EMI data.