%0 Conference Proceedings %T Nuclear counting filter based on a Centered Skellam Test and a double Exponential Smoothing %+ Laboratoire Capteurs et Architectures Electroniques (LCAE) %A Coulon, R. %A Kondrasovs, Vladimir %A Dumazert, J. %A Rohée, E. %A Normand, S. %Z Conference of 4th International Conference on Advancements in Nuclear Instrumentation Measurement Methods and their Applications, ANIMMA 2015 ; Conference Code:121554 %< avec comité de lecture %B 2015 4th International Conference on Advancements in Nuclear Instrumentation Measurement Methods and their Applications (ANIMMA) %C Lisbon, Portugal %I Institute of Electrical and Electronics Engineers Inc. %P 7465500 %8 2015-04-20 %D 2015 %R 10.1109/ANIMMA.2015.7465500 %K Filter %K Signal Processing %K Nuclear %K Measurement %K nuclear counting filter %K Poisson distribution %K radioactivity %K radioactivity measurement %K local maximum likelihood estimation %K nonlinear filter %K stochastic nature %K Brown double exponential smoothing %K centered Skellam test %K maximum likelihood estimation %K Index Terms-Nuclear %Z Physics [physics]/Nuclear Experiment [nucl-ex] %Z Engineering Sciences [physics]/Signal and Image processing %Z Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph] %Z Statistics [stat]/Methodology [stat.ME]Conference papers %X Online nuclear counting represents a challenge due to the stochastic nature of radioactivity. The count data have to be filtered in order to provide a precise and accurate estimation of the count rate, this with a response time compatible with the application in view. An innovative filter is presented in this paper addressing this issue. It is a nonlinear filter based on a Centered Skellam Test (CST) giving a local maximum likelihood estimation of the signal based on a Poisson distribution assumption. This nonlinear approach allows to smooth the counting signal while maintaining a fast response when brutal change activity occur. The filter has been improved by the implementation of a Brown's double Exponential Smoothing (BES). The filter has been validated and compared to other state of the art smoothing filters. The CST-BES filter shows a significant improvement compared to all tested smoothing filters. %G English %2 https://cea.hal.science/cea-01823368/document %2 https://cea.hal.science/cea-01823368/file/article_RomainCoulon_Animma2015_49.pdf %L cea-01823368 %U https://cea.hal.science/cea-01823368 %~ CEA %~ DRT %~ CEA-UPSAY %~ UNIV-PARIS-SACLAY %~ CEA-UPSAY-SACLAY %~ LIST %~ DM2I %~ GS-COMPUTER-SCIENCE %~ DIN