Phase difference estimation method based on data extension and Hilbert transform

Shen, Yong; Tu, Yaqing; Chen, Linjun; Shen, Ting’ao

doi:10.1088/0957-0233/26/9/095003

Cited by 18 publications

(10 citation statements)

References 9 publications

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“…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].…”

Section: Measuring Transient Spontaneous Synchronizationmentioning

confidence: 99%

Synchronization phase as an indicator of persistent quantum correlations between subsystems

Siwiak-Jaszek

Olaya-Castro

2020

Phys. Rev. A

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Spontaneous synchronization is a collective phenomenon that can occur in both dynamical classical and quantum systems. Here, we analyze the spontaneous synchronization dynamics of vibrations assisting energy transfer in a bio-inspired system. We find the emergence of a constant nonzero "synchronization phase" between synchronized vibrational displacements as the natural frequencies of the oscillators are detuned. This phase difference arises from the asymmetric participation of local modes in the long-lived synchronized state. Furthermore, we investigate the relationships between the synchronization phase, detuning and the degree of quantum correlations between the synchronizing subsystems and find that the synchronization phase captures how quantum correlations persistently exceed classical correlations during the dynamics. We show that our analysis applies to a variety of spontaneously synchronizing open quantum systems. Our work therefore opens up a promising avenue to investigate nontrivial quantum phenomena in complex biomolecular and nanoscale chemical systems.

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Section: Measuring Transient Spontaneous Synchronizationmentioning

confidence: 99%

Synchronization phase as an indicator of persistent quantum correlations between subsystems

Siwiak-Jaszek

Olaya-Castro

2020

Phys. Rev. A

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“…In a complex filter, the imaginary filter could be obtained using FFT [23], [24]. However, the number of coefficients compromises the frequency resolution.…”

Section: B Conceptualization Of Complex Brick-wall Band-pass Filtermentioning

confidence: 99%

A New Phasor Estimator for PMU Applications: P Class and M Class

Razo-Hernández

Mejia-Barron

Granados-Lieberman

et al. 2020

Journal of Modern Power Systems and Clean Energy

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Phasor measurement units (PMUs) are fundamental tools in the applications of modern power systems, where synchronized phasor estimations are needed. The accuracy and dynamic performance requirements for phasor, frequency, and rate of change of frequency (ROCOF) estimations are established in the IEEE Std. C37.118.1-2011 along with the IEEE Std. C37.118.1a-2014, where two PMU performances are suggested: P class filters for applications requiring fast response and M class filters for applications requiring high rejection to aliased signals. In this paper, a methodology to design new phasor estimators that satisfy the P class and M class requirements in PMUs is presented. The proposed methodology is based on finite impulse response filters, brick-wall filters, and complex filter design concepts, where frequency range, time performance, harmonic rejection and out-of-band interference requirements are considered in its design. A comparative analysis using the reference model given by the IEEE Std. C37.118.1 is presented. The results show the effectiveness of the phasor estimators under steady-state and dynamic conditions according to the PMU standard, making them suitable tools for applications in power systems.

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“…Regarding the phase difference measurement of sinusoidal signals, many different methods have been proposed, including discrete Fourier transform (DFT) [18,19], digital correlation (DC) [20], Hilbert transform (HT) [21], least squares (LS) [22], independent component analysis (ICA) [23], and zero cross detection (ZCD) [24] based methods. In Reference [18], considering the negative frequency contribution, a new DFT-based algorithm for phase difference measurement of extreme frequency signal is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…This method is named the digital correlation (DC)-based method in this paper. In Reference [21], a phase difference estimation method based on data expansion and HT is proposed. This method obtains the phase difference estimation by data expansion, HT, cross-correlation, autocorrelation, and weighted phase averaging which can suppress the end effect of the HT effectively.…”

Section: Introductionmentioning

confidence: 99%

Phase Difference Measurement of Under-Sampled Sinusoidal Signals for InSAR System Phase Error Calibration

Yuan

Xing

et al. 2019

Sensors

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Phase difference measurement of sinusoidal signals can be used for phase error calibration of the spaceborne single-pass interferometric synthetic aperture radar (InSAR) system. However, there are currently very few papers devoted to the discussion of phase difference measurement of high-frequency internal calibration signals of the InSAR system, especially the discussion of sampling frequency selection and the corresponding measuring method when the high-frequency signals are sampled under the under-sampling condition. To solve this problem, a phase difference measurement method for high-frequency sinusoidal signals is proposed, and the corresponding sampling frequency selection criteria under the under-sampling condition is determined. First, according to the selection criteria, the appropriate under-sampling frequency was chosen to sample the two sinusoidal signals with the same frequency. Then, the sampled signals were filtered by limited recursive average filtering (LRAF) and coherently accumulated in the cycle of the baseband signal. Third, the filtered and accumulated signals were used to calculate the phase difference of the two sinusoidal signals using the discrete Fourier transform (DFT), digital correlation (DC), and Hilbert transform (HT)-based methods. Lastly, the measurement accuracy of the three methods were compared respectively by different simulation experiments. Theoretical analysis and experiments verified the effectiveness of the proposed method for the phase error calibration of the InSAR system.

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Phase difference estimation method based on data extension and Hilbert transform

Cited by 18 publications

References 9 publications

Synchronization phase as an indicator of persistent quantum correlations between subsystems

Synchronization phase as an indicator of persistent quantum correlations between subsystems

A New Phasor Estimator for PMU Applications: P Class and M Class

Phase Difference Measurement of Under-Sampled Sinusoidal Signals for InSAR System Phase Error Calibration

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