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CMF Signal Processing Method Based on Feedback Corrected ANF and Hilbert Transformation

机译：基于反馈校正的ANF和希尔伯特变换的CMF信号处理方法

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In this paper, we focus on CMF signal processing and aim to resolve the problems of precision sharp-decline occurrence when using adaptive notch filters (ANFs) for tracking the signal frequency for a long time and phase difference calculation depending on frequency by the sliding Goertzel algorithm (SGA) or the recursive DTFT algorithm with negative frequency contribution. A novel method is proposed based on feedback corrected ANF and Hilbert transformation. We design an index to evaluate whether the ANF loses the signal frequency or not, according to the correlation between the output and input signals. If the signal frequency is lost, the ANF parameters will be adjusted duly. At the same time, singular value decomposition (SVD) algorithm is introduced to reduce noise. And then, phase difference between the two signals is detected through trigonometry and Hilbert transformation. With the frequency and phase difference obtained, time interval of the two signals is calculated. Accordingly, the mass flow rate is derived. Simulation and experimental results show that the proposed method always preserves a constant high precision of frequency tracking and a better performance of phase difference measurement compared with the SGA or the recursive DTFT algorithm with negative frequency contribution.
机译：本文着重于CMF信号处理，旨在解决使用自适应陷波滤波器（ANF）长时间跟踪信号频率以及根据滑动Goertzel的频率进行相位差计算时精度急剧下降的问题算法（SGA）或具有负频率贡献的递归DTFT算法。提出了一种基于反馈校正的ANF和希尔伯特变换的新方法。我们根据输出和输入信号之间的相关性，设计一个指标来评估ANF是否丢失信号频率。如果信号频率丢失，则将适当调整ANF参数。同时，引入奇异值分解（SVD）算法以减少噪声。然后，通过三角函数和希尔伯特变换检测两个信号之间的相位差。利用获得的频率和相位差，可以计算两个信号的时间间隔。因此，得出质量流量。仿真和实验结果表明，与SGA或具有负频率贡献的递归DTFT算法相比，该方法始终保持恒定的高精度频率跟踪和更好的相位差测量性能。

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