Correlation theory-based signal processing method for CMF signals

Abstract

Signal processing precision of Coriolis mass flowmeter (CMF) signals affects measurement accuracy of Coriolis mass flowmeters directly. To improve the measurement accuracy of CMFs, a correlation theory-based signal processing method for CMF signals is proposed, which is comprised of the correlation theory-based frequency estimation method and phase difference estimation method. Theoretical analysis shows that the proposed method eliminates the effect of non-integral period sampling signals on frequency and phase difference estimation. The results of simulations and field experiments demonstrate that the proposed method improves the anti-interference performance of frequency and phase difference estimation and has better estimation performance than the adaptive notch filter, discrete Fourier transform and autocorrelation methods in terms of frequency estimation and the data extension-based correlation, Hilbert transform, quadrature delay estimator and discrete Fourier transform methods in terms of phase difference estimation, which contributes to improving the measurement accuracy of Coriolis mass flowmeters.

A Frequency-Time Algorithm of Parameter Estimation for Sinusoidal Signal in Noise

“…Variable SNRs: we first examine the frequency estimation performance versus SNRs, varying from -10 to 50 dB, in steps of 2 dB. In addition, 0 2 k  , and  is random from -0.5 to 0.5. The numerical results are shown in Fig.…”

“…Accurate parameter estimation for real-value sinusoidal signal in additive white Gaussian noise is a fundamental problem in a wide range of fields, and the studies of parameter estimation algorithms have an important theoretical significance and application value [1], [2]. The samples of a single-tone sinusoidal signal can be described as:…”

Synchronization phase as an indicator of persistent quantum correlations between subsystems

“…This measure is used repeatedly throughout the paper as a dynamic measure of phase and allows us to connect synchronization phase to the Hamiltonian structure and quantum correlations in novel ways. Other attempts to develop a realtime phase measure between two oscillating signals have been conducted along the lines of sliding-window discrete Fourier transform methods [43], Hilbert transforms with data extension [44], and other correlation functions [45].…”

Synchronisation phase as an indicator of persistent quantum correlations between subsystems

“…This measure is used repeatedly throughout the paper as a dynamic measure of phase and allows us to connect synchronisation phase to the Hamiltonian structure and quantum correlations in novel ways. Other attempts to develop a real-time phase measure between two oscillating signals have been conducted along the lines of slidingwindow discrete Fourier transform methods [44], Hilbert transforms with data extension [45] and other correlation functions [46].…”