Christopher Davis
Pronouns:
Research Mentor(s): Kimberlee Kearfott
Co-Presenter:
Research Mentor School/College/Department: NERS/BME / Engineering
Presentation Date: April 20
Presentation Type: Poster
Session: Session 1 – 10am – 10:50am
Room: League Ballroom
Authors: Christopher C Davis, Marlee Trager, LongKiu Chung, Ryan A Kim, Jordan Noey, Kimberlee Kearfott
Presenter: 73
Abstract
It would be extremely helpful to have the ability of rapidly and remotely mapping radionuclide distributions of contaminated outdoor areas resulting from current or legacy nuclear operations or accidents. Such maps are also required following decommissioning or cleanup operations, for which often only random soil sampling is conducted. An autonomous unmanned aerial vehicle (UAV) carrying a radiation sensor payload is currently being designed and built for this purpose. This specific presentation is focused on algorithms envisioned for interfacing with the navigational software of a small UAV to guide the drone’s path based upon reconstructed maps of the locations of radioactive sources producing the dose levels at different points already visited and measured. Creating maps of radionuclide sources from measured dose rates at different points is fundamentally an image-reconstruction problem, which may be solving using simple brute-force iterative approaches. The problem readily lends itself to Least Squares, Recursive Bayesian Estimation, and Maximum A Posteriori (MAP) methods. The MAP estimator depends upon resolution, grid placement, and an appropriate prior, making it have less promise than the other methods. The problem of source localization may also be treated as a Hill-Climbing problem, with coordinate descent and stochastic methods proving to be particularly efficient. One especially difficult challenge is when multiple sources are located in close proximity. Frequency analysis and the power spectral density may be used to discriminate signals in such cases. A comparison of these methods, based upon simulations, will be presented.
Engineering, Interdisciplinary
