A two-dimensional filtering method, based on the construction of systems of approximating sliding local model functions with their subsequent weighted averaging, is proposed. A two-dimensional filtering algorithm based on the summation of approximating sliding piecewise-linear model functions with weight coefficients is developed. Optimization of the parameters of the filtering algorithm is considered. The results of testing the filtering algorithm and calculating its optimal parameters on model muonograms are presented. A comparative estimation of the algorithm’s errors is made. An example of testing the developed method of two-dimensional filtering on an experimental muonogram is presented; the results of the muonogram’s analysis are described.

Geomagnetic pulsations of the serpentine-emission (SE) type are considered. A method for estimating the frequency and amplitude parameters in the form of a time function for pulsations—SE and the...

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.

A method for local intensity anisotropy recognition in muon fluxes is proposed. The method is based on the use of matrix observations of the URAGAN hodoscope. Systems of confidence intervals are calculated for the estimates of mathematical expectations of Poisson samples. The anomaly indicators are generated for reference and current segments of time series of matrix observations using the confidence intervals. Indicator matrices based on the anomaly indicators are introduced. Spatiotemporal filtering of indicator matrices is implemented in order to recognize local anisotropies. The developed method is tested on model and experimental observations.