Low-Pass Filtering Method for Poisson Data Time Series
2021
Victor Getmanov | Vladislav Chinkin | Roman Sidorov | Alexei Gvishiani | Mikhail Dobrovolsky | Anatoly Soloviev | Anna Dmitrieva | Anna Kovylyaeva | Nataliya Osetrova | Igor Yashin
Problems of digital processing of Poisson-distributed data time series from various counters of radiation particles, photons, slow neutrons etc. are relevant for experimental physics and measuring technology. A low-pass filtering method for normalized Poisson-distributed data time series is proposed. A digital quasi-Gaussian filter is designed, with a finite impulse response and non-negative weights. The quasi-Gaussian filter synthesis is implemented using the technology of stochastic global minimization and modification of the annealing simulation algorithm. The results of testing the filtering method and the quasi-Gaussian filter on model and experimental normalized Poisson data from the URAGAN muon hodoscope, that have confirmed their effectiveness, are presented.
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