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Online tracking of instantaneous frequency and amplitude of dynamical system response

机译:在线跟踪动态系统响应的瞬时频率和幅度

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This paper presents a sliding-window tracking (SWT) method for accurate tracking of the instantaneous frequency and amplitude of arbitrary dynamic response by processing only three (or more) most recent data points. Teager-Kaiser algorithm (TKA) is a well-known four-point method for online tracking of frequency and amplitude. Because finite difference is used in TKA, its accuracy is easily destroyed by measurement and/or signal-processing noise. Moreover, because TKA assumes the processed signal to be a pure harmonic, any moving average in the signal can destroy the accuracy of TKA. On the other hand, because SWT uses a constant and a pair of windowed regular harmonics to fit the data and estimate the instantaneous frequency and amplitude, the influence of any moving average is eliminated. Moreover, noise filtering is an implicit capability of SWT when more than three data points are used, and this capability increases with the number of processed data points. To compare the accuracy of SWT and TKA, Hilbert-Huang transform is used to extract accurate time-varying frequencies and amplitudes by processing the whole data set without assuming the signal to be harmonic. Frequency and amplitude trackings of different amplitude-and frequency-modulated signals, vibrato in music, and nonlinear stationary and non-stationary dynamic signals are studied. Results show that SWT is more accurate, robust, and versatile than TKA for online tracking of frequency and amplitude.
机译:本文提出了一种滑动窗口跟踪(SWT)方法,可通过仅处理三个(或更多)最新数据点来准确跟踪任意动态响应的瞬时频率和幅度。 Teager-Kaiser算法(TKA)是一种在线跟踪频率和幅度的著名的四点方法。由于TKA中使用了有限差分,因此其精度很容易被测量和/或信号处理噪声所破坏。此外,由于TKA假定处理后的信号为纯谐波,因此信号中的任何移动平均值都会破坏TKA的准确性。另一方面,由于SWT使用常数和一对加窗的规则谐波来拟合数据并估计瞬时频率和幅度,因此消除了任何移动平均值的影响。此外,当使用三个以上的数据点时,噪声过滤是SWT的隐式功能,并且此功能随处理的数据点的数量而增加。为了比较SWT和TKA的精度,使用Hilbert-Huang变换通过处理整个数据集来提取准确的时变频率和幅度,而无需假设信号是谐波。研究了不同幅度和频率调制信号,音乐中的颤音以及非线性平稳和非平稳动态信号的频率和幅度跟踪。结果表明,对于频率和幅度的在线跟踪,SWT比TKA更准确,更健壮,更通用。

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