СРАВНИТЕЛЬНАЯ ОЦЕНКА МЕТОДОВ УСРЕДНЕНИЙ ДЛЯ ФИЛЬТРАЦИИ ИЗМЕРИТЕЛЬНЫХ СИГНАЛОВ

Abstract

To improve the quality of manufactured products, it is necessary to improve all technologicalprocesses, which requires increasing the accuracy of the entire measuring path as a whole.For this it is necessary to carefully analyze systematic, random and fluctuating errors in the measurementchannel and take all measures to reduce them. Digital filtering or averaging of intermediatemeasurements (observations) according to certain rules is a radical means of improving theaccuracy of measurements performed. The aim of this work is to compare the quality of suppressionof near-real noise interference using the eight most well-known averaging methods. A model of the measurement path and a general block diagram for modeling the measurement process on acomputer under the influence of random interference are proposed for eight averaging algorithms.As a criterion for evaluating the quality of averaging methods, the ratios of absolute error variancesand mean square deviations before the computing device and after applying the specified averagingalgorithm are taken. Based on the simulation results, the following conclusions are made. 1. All averagingalgorithms provide suppression of random error components of complex interference to thelevel of 40–60 dB. Three algorithms are the best: arithmetic mean AR, a-truncated mean AU5 and atenderizedmean AB5, which provide for the suppression of 5 % of anomalous results. With an increasein the number of observations, the suppression coefficients increase proportionally. 2. Thesampling time must be a multiple of the duration of the 50 Hz AC mains period (20 ms). The optimalnumber of observations (measurements) is 100–128; with 128 measurements, the division operationis reduced to a simple shift, and the averaging result can be obtained in 1–2 μs. 3. When experimentallyapplying the AR averaging method for filtering a highly noisy measurement signal in a communicationline with a length of 800 m, a decrease in the spread of ADC output codes was observedfrom ± 3.5 % to ± 0.1 % after filtering (AR, 64 measurements in 40 ms).