Improved UXO detection via sensor fusion

Traditional algorithms for UXO remediation experience severe difficulties distinguishing buried targets from anthropic clutter, and in most cases UXO items are found amongst 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 over the past several years 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 characteristic of the sensor and the anticipated UXO target in order to address the false alarm issue. In this paper, we describe several techniques for discriminating targets from clutter that have been applied to data obtained with the Multisensor Towed Array Detection System (MTADS) that has been developed by the Naval Research Laboratory. 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 will 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. For example, in the JPG-IV test, at the False Positive level obtained by NRL, one of our algorithms achieved a 13% improvement in True Positive level over the algorithm traditionally used for processing MTADS data.