Vajpayee, V. orcid.org/0000-0003-1179-7118, Becerra, V., Bausch, N. et al. (3 more authors) (2019) Estimation of radionuclide release activity using an Unscented Kalman Filter. In: 2019 6th International Conference on Instrumentation, Control, and Automation (ICA). 6th International Conference on Instrumentation, Control, and Automation (ICA), 31 Jul - 02 Aug 2019, Bandung, Indonesia. Institute of Electrical and Electronics Engineers (IEEE) , pp. 231-236. ISBN 9781728109176
Abstract
Estimation of radionuclide release is an important problem due to its impact on population and environment. Especially, radioactivity release, plume height, and wind velocity need to be estimated reliably to plan emergency response in case of any unforeseen situation. In this paper, a non-linear estimation technique based on Unscented Kalman Filter has been proposed to estimate radioactivity release, wind velocity, and height of release using environmental data collected from radiation monitors placed in the proximity of release point. The Gaussian plume model has been considered to model atmospheric dispersion phenomenon of radionuclide release and for the calculation of dose rates. The performance of the proposed estimation technique has been evaluated in terms of root mean squared error. The estimation algorithm is found to be performing satisfactorily.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. |
Keywords: | Atmospheric Dispersion; Dose Rate; Gaussian Plume Model; Radionuclide Release; Unscented Kalman Filter |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Sep 2022 13:50 |
Last Modified: | 27 Sep 2022 13:50 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/ICA.2019.8916744 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190555 |