Estimation of Nuclear Counting by a Nonlinear Filter Based on a Hypothesis Test and a Double Exponential Smoothing

Abstract

Online nuclear counting represents a challenge due to the stochastic nature of radioactivity. The counting data have to be filtered in order to provide a precise and accurate estimation of the count rate, while ensuring a response time compatible with the application in view. An innovative filter is presented in this paper to address this issue. The filter is nonlinear and based on a Centered Significance Test (CST) providing a local maximum likelihood estimation of the signal. This nonlinear approach allows enables to smooth the counting signal while maintaining a fast response when brutal change in activity occurs. The filter is then 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* filter shows a significant improvement compared to all tested smoothing filters.