State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2022 Apr 20;22(9):3160. doi: 10.3390/s22093160.
In this work, an adaptive generalized cross-correlation (AGCC) method is proposed that focuses on the problem of the conventional cross-correlation method not effectively realizing the time delay estimation of signals with strong periodicity. With the proposed method, the periodicity of signals is judged and the center frequencies of the strongly periodical components are determined through the spectral analysis of the input signals. Band-stop filters that are used to suppress the strongly periodical components are designed and the mutual power spectral density of the input signals that is processed by the band-stop filters is calculated. Then, the cross-correlation function that is processed is the inverse Fourier transform of the mutual power spectral density. Finally, the time delay is estimated by seeking the peak position of the processed cross-correlation function. Simulation experiments and practical velocity measurement experiments were carried out to verify the effectiveness of the proposed AGCC method. The experimental results showed that the new AGCC method could effectively realize the time delay estimation of signals with strong periodicity. In the simulation experiments, the new method could realize the effective time delay estimation of signals with strong periodicity when the energy ratio of the strongly periodical component to the aperiodic component was under 150. Meanwhile, the cross-correlation method and other generalized cross-correlation methods fail in time delay estimation when the energy ratio is higher than 30. In the practical experiments, the velocity measurement of slug flow with strong periodicity was implemented in small channels with inner diameters of 2.0 mm, 2.5 mm and 3.0 mm. With the proposed method, the relative errors of the velocity measurement were less than 4.50%.
在这项工作中，提出了一种自适应广义互相关（AGCC）方法，该方法主要针对传统互相关方法无法有效实现强周期性信号时延估计的问题。在提出的方法中，通过对输入信号的频谱分析来判断信号的周期性，并确定强周期性分量的中心频率。设计了用于抑制强周期性分量的带阻滤波器，并计算经过带阻滤波器处理的输入信号的互功率谱密度。然后，对互功率谱密度进行处理的互相关函数是互功率谱密度的逆傅里叶变换。最后，通过寻找处理后的互相关函数的峰值位置来估计时延。通过仿真实验和实际速度测量实验验证了所提出的 AGCC 方法的有效性。实验结果表明，新的 AGCC 方法可以有效地实现强周期性信号的时延估计。在仿真实验中，当强周期性分量与非周期性分量的能量比低于 150 时，新方法可以实现强周期性信号的有效时延估计。同时，当能量比高于 30 时，互相关方法和其他广义互相关方法无法进行时延估计。在实际实验中，在直径为 2.0mm、2.5mm 和 3.0mm 的小通道中实现了强周期性段塞流的速度测量。通过提出的方法，速度测量的相对误差小于 4.50%。
