Autocorrelation-based algorithm for single-frequency estimation

“…According to Proakis [9], the autocorrelation function of finite discrete sequences can be described by Equation (12); however, if the discrete sequence is periodic with a period consisting of N samples, then the autocorrelation can be described by Equation (B1). Taking M samples of the signal in Equation (B2), where xpnq is a periodic sequence of unknown period and wpnq represents a random interference, the autocorrelation can be described by Equation (B3).…”

Section: Appendix Bmentioning

confidence: 99%

“…In some practical applications, the autocorrelation function [9][10][11] is used to identify the periodicities in an observed physical signal, which may be corrupted by a random interference [9]. In some recent studies, the problem of estimating the signal parameters for a sinusoidal waveform immersed in random noise has been addressed by using the autocorrelation function [12][13][14]. There exists the non-parametric methods, which are based on the idea of estimating the autocorrelation sequence of a random process from a set of measured data and then taking the Fourier transform for estimating the power spectrum; and the parametric methods, which require some knowledge about how the data samples are generated [15,16].…”

Section: Introductionmentioning

confidence: 99%

Automatic Frequency Identification under Sample Loss in Sinusoidal Pulse Width Modulation Signals Using an Iterative Autocorrelation Algorithm

Said

Davizón

Román³

et al. 2016

Symmetry

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Abstract:In this work, we present a simple algorithm to calculate automatically the Fourier spectrum of a Sinusoidal Pulse Width Modulation Signal (SPWM). Modulated voltage signals of this kind are used in industry by speed drives to vary the speed of alternating current motors while maintaining a smooth torque. Nevertheless, the SPWM technique produces undesired harmonics, which yield stator heating and power losses. By monitoring these signals without human interaction, it is possible to identify the harmonic content of SPWM signals in a fast and continuous manner. The algorithm is based in the autocorrelation function, commonly used in radar and voice signal processing. Taking advantage of the symmetry properties of the autocorrelation, the algorithm is capable of estimating half of the period of the fundamental frequency; thus, allowing one to estimate the necessary number of samples to produce an accurate Fourier spectrum. To deal with the loss of samples, i.e., the scan backlog, the algorithm iteratively acquires and trims the discrete sequence of samples until the required number of samples reaches a stable value. The simulation shows that the algorithm is not affected by either the magnitude of the switching pulses or the acquisition noise.

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“…For its determination we are applying methods that can be divided into two main categories. The spectral methods (interpolated DFT methods [1][2][3][4][5][6], cepstral method [7], ACOLS method [8], methods in which frequency estimation is carried out by means of maximum likelihood estimators [9]) and the time methods (correlation methods [10][11][12], threshold methods [13], methods using the digital filters [14], Bayesian methods [15], point methods [16][17][18]) can be pointed out.…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Sienkowski¹,

Krajewski²

2018

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In this paper, two new sinusoidal signal frequency estimators calculated on the basis of four equally spaced signal samples are presented. These estimators are called four-point estimators. Simulation and experimental research consisting in signal frequency estimation using the invented estimators have been carried out. Simulation has also been performed for frequency tracking. The simulation research was carried out applying the MathCAD computer program that determined samples of a sinusoidal signal disturbed by Gaussian noise. In the experimental research, sinusoidal signal samples were obtained by means of a National Instruments PCI-6024E data acquisition card and an Agilent 33220A function generator. On the basis of the collected samples, the values of four-point estimators invented by the authors and, for comparison, the values of three-and four-point estimators proposed by Vizireanu were determined. Next, estimation errors of the signal frequency were determined. It has been shown that the invented estimators can estimate a signal frequency with greater accuracy.

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Autocorrelation-based algorithm for single-frequency estimation

Cited by 16 publications

References 11 publications

Frequency Estimation Method From Digitized Waveform

Frequency Estimation Method From Digitized Waveform

Automatic Frequency Identification under Sample Loss in Sinusoidal Pulse Width Modulation Signals Using an Iterative Autocorrelation Algorithm

Metrology and Measurement Systems

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