Optimal Sensor Management and Signal Processing for New EMI Systems
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.
When adaptively performing sensing with the objective of optimally inferring the model parameters, information-based search algorithms are used. These techniques select to measure that data for which there is the greatest expected reduction in model-parameter uncertainty; these approaches require no a priori knowledge of the types of buried items under test. When sensing with the objective of directly making a classification decision (e.g., after another sensor – possibly magnetometer – has detected the item), it is assumed that the types of target/clutter under test are known (i.e., training data exists). This second approach is implemented using the state-of-the-art POMDP algorithm.
The adaptive sensor-management algorithms will allow one to retain the sophistication of complex multi-coil systems, while still achieving fast sensor collections, since typically only a subset of the full sensor functionality will be applied to a given target. Further, this research has the potential of optimizing the use of handheld systems, assuring appropriate data have been collected for parameter inversion. (Anticipated Project Completion - 2012)
Points of Contact
Dr. Lawrence Carin
SERDP and ESTCP
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