Objective

A significant challenge associated with new electromagnetic induction (EMI) systems is the time required to collect all data, and this limitation often necessitates a priori hardware compromises that undermine subsequent system performance. Adaptive approaches would allow one to retain full EMI hardware functionality, with sensing speed attained by adaptively tailoring the sensor use to the item under test. Under the SERDP Exploratory Development (SEED) project MR-1591, researchers developed two adaptive sensing techniques. The first approach assumes no a priori knowledge of the particular targets and clutter under test, and it adaptively performs measurements with the objective of best inferring the EMI model parameters of the buried item (without necessarily knowing its location a priori). The second approach performs adaptive sensing with the objective of making a final classification decision, assuming that one knows the types of clutter and UXO under test. The advantage of these approaches is that they allow optimal, adaptive use of sensor resources.

The objective of this project is to develop an adaptive sensor management architecture of interest for two applications: (1) optimal use of sensor assets for sophisticated next-generation (e.g., multi-coil) systems such as the TEM array, and (2) guiding the use of emerging portable (handheld) systems on where to collect new data and when to terminate sensing. The research constitutes active learning with the purpose of optimally using sensor assets. To support this objective, researchers have investigated the partially observed Markov decision process (POMDP) and related information-based sensing algorithms. In this project, they will develop a fully Bayesian approach while designing a sensing policy, incorporating both prior knowledge about the site and uncertainties about the model.