Objective

This research program was focused on advanced technologies for detection and discrimination of military munitions. The underlying premise of the program was that there is an inherent limitation in the information content associated with magnetometer and electromagnetic induction (EMI) sensors deployed for unexploded ordnance (UXO) cleanup. To optimize UXO classification, one must integrate all available information, both within the measured data itself and within a priori knowledge one may possess.

An important class of prior knowledge is represented by the sensor physics, and by placing as much physics as possible into the models and classification features, one removes the need to rely on the limited sensor data to infer such phenomenology. Statistical classifiers are also required to maximize the information extracted from the measured data to infer the unknown model parameters. Further, the statistical classifiers may be used to appropriately exploit other forms of information inherent to the data. For example, while performing classification one may exploit the contextual information provided by all of the unlabeled data at a given site, while also appropriately leveraging related information in data measured at previous  sites.

The overall objective of the research was to integrate advanced Bayesian statistical models and classifiers with leading geophysical models to enhance the ability to extract information from limited sensor data, with the goal of markedly improving UXO classification performance on complex cleanup missions.