Low-Pass Filtering Method for Poisson Data Time Series

被引：1

作者：

Getmanov, Victor ^{[1
,2
]}

Chinkin, Vladislav ^{[1
]}

Sidorov, Roman ^{[1
]}

Gvishiani, Alexei ^{[1
,2
]}

Dobrovolsky, Mikhail ^{[1
]}

Soloviev, Anatoly ^{[1
,2
]}

Dmitrieva, Anna ^{[1
,3
]}

Kovylyaeva, Anna ^{[1
,3
]}

Osetrova, Nataliya ^{[1
,3
]}

Yashin, Igor ^{[1
,3
]}

机构：

[1] Russian Acad Sci GC RAS, Geophys Ctr, Moscow 119296, Russia

[2] Russian Acad Sci IPE RAS, Schmidt Inst Phys Earth, Moscow 123242, Russia

[3] Natl Res Nucl Univ MEPhI NRNU MEPhI, Sci & Educ Ctr NEVOD, Moscow 115409, Russia

来源：

APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 10期

基金：

俄罗斯科学基金会;

关键词：

Poisson data; time series; quasi-Gaussian filter; digital filtering; optimization; global minimization; annealing simulation algorithm;

D O I：

10.3390/app11104524

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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.

引用

页数：12

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