Information-based sensor management for the intelligent tasking of ground penetrating radar and electromagnetic induction sensors in landmine detection pre-screening

Previous work has introduced a framework for information-based sensor management that is capable of tasking multiple sensors searching for targets among a set of discrete objects or in a cell grid. However, in many real-world scenarios - such as detecting landmines along a lane or road - an unknown number of targets are present in a continuous spatial region of interest. Consequently, this paper introduces a grid-free sensor management approach that allows multiple sensors to be managed in a sequential search for targets in a grid-free spatial region. Simple yet expressive Gaussian target models are introduced to model the spatial target responses that are observed by the sensors. The sensor manager is then formulated using a Bayesian approach, and sensors are directed to make new observations that maximize the expected information gain between the posterior density on the target parameters after a new observation and the current posterior target parameter density. The grid-free sensor manager is applied to a set of real landmine detection data collected with ground penetrating radar (GPR) and electromagnetic induction (EMI) sensors at a U.S. government test site. Results are presented that compare the performance of the sensor manager with the performance of an unmanaged joint pre-screener that fuses individual GPR and EMI pre-screeners. The sensor manager is demonstrated to provide improved detection performance while requiring substantially fewer sensor observations than are made with the unmanaged joint pre-screening approach. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Duke Scholars

Author Leslie M. Collins Electrical and Computer Engineering