Estimation of radioactivity release activity using non-linear Kalman filter-based estimation techniques

Victor Becerra*, Vineet Vajpayee, Nils Bausch, T. V. Santhosh, Gopika Vinod, Jiamei Deng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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The estimation of radioactivity release following an accident in a nuclear power plant is crucial due to its short and long-term impacts on the surrounding population and the environment. In the case of any accidental release, the activity needs to be estimated quickly and reliably to effectively plan a rapid emergency response and design an appropriate evacuation strategy. The accurate prediction of incurred dose rate during normal or accident scenario is another important aspect. In this article, three different non-linear estimation techniques, extended Kalman filter, unscented Kalman filter, and cubature Kalman filter are proposed in order to estimate release activity and to improve the prediction of dose rates. Radionuclide release rate, average wind speed, and height of release are estimated using the dose rate monitors data collected in proximity of the release point. Further, the estimates are employed to improve the prediction of dose rates. The atmospheric dispersion phenomenon of radioactivity release is modelled using the Gaussian plume model. The Gaussian plume model is then employed for the calculation of dose rates. A variety of atmospheric and accident related scenarios for single source and multiple sources are studied in order to assess the efficacy of the proposed filters. Statistical measures have been used in order to validate the performance of the proposed approaches.
Original languageEnglish
Article number3985
Number of pages23
Issue number15
Publication statusPublished - 2 Aug 2020


  • RCUK
  • EP/M018709/1
  • atmospheric dispersion model
  • dose rate
  • estimation technique
  • Gaussian plume model
  • radioactivity release
  • extended Kalman filter
  • unscented Kalman filter
  • cubature Kalman filter


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