The use of the numbers of zero-level crossings to detect a periodicity with known frequency in a noisy signal

Levenets, A. V.; Un, Chye En

doi:10.1007/s11018-013-0286-y

Cited by 3 publications

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“…3). It can be used both to estimate the spectrum of the signal being studied, and for other practical tasks, in particular the detection of periodic signals [4]. There is a signifi cant limitation to the quasispectrum; it has low sensitivity at the edges of the frequency band, and this must be taken into account in choosing the sampling rate [3,5].…”

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Method for Evaluating Periodic Trends in Measured Signals Based on the Number of Zero Crossings

Levenets

2015

Meas Tech

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A method is proposed for evaluating the frequency of periodic trends based on the number of zero crossings by a signal. This method is distinguished by low computation time requirements. Test results demonstrate the possible use of this method in practical applications.Initial data processing is needed in measurement-data systems to reduce the volume of data transmitted along communication lines. An important aspect of this processing is the detection of trends with their subsequent elimination. Identifying a constant component and trends of a simple linear form is not diffi cult and procedures for detecting these are well known and widely used [1]. A more diffi cult problem, at least computationally, is the identifi cation of periodic trends; to determine the parameters of these trends spectral estimation methods, such as periodograms, correlograms, fast Fourier transform, etc., are used. The main shortcoming of these methods is long computational time, especially for the periodogram technique. The amount of calculations can be lowered if some a priori information on the frequency of a harmonic trend, which can be obtained using an easily executed rough frequency analysis, is available.Attention should be drawn to signal analysis in terms of the number of zero crossings (NZC), which can be used for spectral analysis of signals [2]. It is proposed that simple repetitive-difference and repetitive-summing fi lters be used as a fi lter. In the fi rst order, their operation reduces to successive subtraction (addition) of neighboring readouts of the initial data sequence, and application of this operation to the already fi ltered sequence is a second order fi lter, etc. A repetitive-difference fi lter is a high frequency fi lter, while a repetitive-summing fi lter is a low frequency fi lter that is simpler to use and, therefore, takes less computational time, so it can be used in microcontroller devices. In realizing repetitive-difference and -summation fi lters, it should be remembered that the sample volume at the output from the kth order fi lter is k readouts smaller than at the input.Estimating the Frequency of a Periodic Trend. Since NZC methods are associated with the spectral function of the analyzed signal, it is proposed that this fact be used for a rough spectral analysis [3]. To do this, repetitive-difference and -summation fi lters are applied to the signal sequentially in different combinations (the maximum order of fi lters is limited) and the number of zero crossings is calculated for each combination of these fi lters. The set of numbers of zero crossings obtained after applying a series of the simplest fi lters to the signal can be regarded as a sort of spectral function (referred to as a quasispectrum in Ref. 3). It can be used both to estimate the spectrum of the signal being studied, and for other practical tasks, in particular the detection of periodic signals [4]. There is a signifi cant limitation to the quasispectrum; it has low sensitivity at the edges of the frequency band, and this must be ...

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confidence: 99%