A new phase difference measurement algorithm for extreme frequency signals based on discrete time Fourier transform with negative frequency contribution

Shen, Ting’ao; Tu, Yaqing; Li, Ming; Zhang, Haitao

doi:10.1063/1.4904971

Cited by 12 publications

(3 citation statements)

References 11 publications

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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. The phase difference calculation formula under different windows is deduced in detail.…”

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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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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“…Simultaneously, they proved that the proposed estimator was nearly unbiased and derived the formula for the estimation of error variance. In 2015, aiming at the estimation of ultralow frequency or adjacent Nyquist frequency, Shen and Tu presented a new phase difference measurement algorithm by considering the negative frequency contribution [19]. Recently, Luo proposed an estimation algorithm based on asymmetric windows, which can get the phase difference through one segment in the time domain [1,20].…”

Section: Introductionmentioning

confidence: 99%

An improved phase interpolation estimator

Luo

Zhou

et al. 2019

IEEJ Transactions Elec Engng

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The capability of time-shifting-based phase difference method against noise can be considerably improved with the increase of the translation coefficient. However, the anti-noise performance of the traditional method is limited due to the wrapped phase problem. In order to obtain high accuracy of estimate, this paper proposes an improved phase interpolation estimator which is derived in terms of the time-shifting-based phase difference method. First, the estimator utilizes three segments of signal in the time domain to build two sets of frequency estimate. Then the absolute difference of the frequency estimate between the two sets can be calculated. Subsequently, four frequency estimates with minimal absolute difference are selected. Finally, the one in four frequencies owning maximum energy is selected as the final frequency estimate. Simulation results show that the improved algorithm can overcome the wrapped phase problem to some extent and can achieve better noise immunity than traditional algorithms.

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“…The line of the relatively large amplitude next to 1 k is called second-rate amplitude value [9]. Assume the discrete frequency index value is 2 k , the second-rate amplitude value 2 A can be approximated as: …”

Section: ) Convergence Problemmentioning

confidence: 99%

A Novel Adaptive Frequency Estimation Algorithm Based on Interpolation FFT and Improved Adaptive Notch Filter

Shen

Zhang

et al. 2017

Measurement Science Review

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The convergence rate and the continuous tracking precision are two main problems of the existing adaptive notch filter (ANF) for frequency tracking. To solve the problems, the frequency is detected by interpolation FFT at first, which aims to overcome the convergence rate of the ANF. Then, referring to the idea of negative feedback, an evaluation factor is designed to monitor the ANF parameters and realize continuously high frequency tracking accuracy. According to the principle, a novel adaptive frequency estimation algorithm based on interpolation FFT and improved ANF is put forward. Its basic idea, specific measures and implementation steps are described in detail. The proposed algorithm obtains a fast estimation of the signal frequency, higher accuracy and better universality qualities. Simulation results verified the superiority and validity of the proposed algorithm when compared with original algorithms.

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A new phase difference measurement algorithm for extreme frequency signals based on discrete time Fourier transform with negative frequency contribution

Cited by 12 publications

References 11 publications

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

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

An improved phase interpolation estimator

A Novel Adaptive Frequency Estimation Algorithm Based on Interpolation FFT and Improved Adaptive Notch Filter

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