New Adaptive Methods for Particles Flux Intensity Measurement Redundancy Reduction and Their Efficiency

Abstract

Particles flux intensity measurements redundancy reduction algorithms are proposed. Accuracy criteria consists in limiting of a samples relative error maximum value. The algorithms are based on prediction or interpolation operations with a variable threshold, adaptive to a changing flux intensity. A formula for computation of an adaptive threshold zero order predictor compression ratio is deduced. Computed values show good coincidence with those received by signal and algorithm computer simulation. Adapter threshold zero order predictor (AT-ZOP) and first order interpolator (AT-FOI) algorithms applied to real telemetry data reveal their high efficiency as relating to attainable compression ratios. Algorithms compression ratio comparison results in predictor advantage against interpolator and unsignificantly small predictor loss when preliminary data smoothing is applied. Compression ratios for joint application of background removal [2] and adaptive predictor algorithms are also evaluated. AT-ZOP simplicity and high efficiency allow to recommend it for use in particle flux intensity measurements redundancy reduction systems.