Gao Yang, Tian Dapeng, Wang Yutang
Key laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2020 Feb 10;20(3):948. doi: 10.3390/s20030948.
Sensor differential signals are widely used in many systems. The tracking differentiator (TD) is an effective method to obtain signal differentials. Differential calculation is noise-sensitive. There is the characteristics of low-pass filter (LPF) in the TD to suppress the noise, but phase lag is introduced. For LPF, fixed filtering parameters cannot achieve both noise suppression and phase compensation lag compensation. We propose a fuzzy self-tuning tracking differentiator (FSTD) capable of adaptively adjusting parameters, which uses the frequency information of the signal to achieve a trade-off between the phase lag and noise suppression capabilities. Based on the frequency information, the parameters of TD are self-tuning by a fuzzy method, which makes self-tuning designs more flexible. Simulations and experiments using motion measurement sensors show that the proposed method has good filtering performance for low-frequency signals and improves tracking ability for high-frequency signals compared to fixed-parameter differentiator.
传感器差分信号在许多系统中被广泛使用。跟踪微分器（TD）是获取信号微分的一种有效方法。差分计算对噪声敏感。TD具有低通滤波器（LPF）的特性来抑制噪声，但会引入相位滞后。对于LPF，固定的滤波参数无法同时实现噪声抑制和相位补偿滞后补偿。我们提出了一种能够自适应调整参数的模糊自整定跟踪微分器（FSTD），它利用信号的频率信息在相位滞后和噪声抑制能力之间进行权衡。基于频率信息，通过模糊方法对TD的参数进行自整定，这使得自整定设计更加灵活。使用运动测量传感器进行的仿真和实验表明，与固定参数微分器相比，该方法对低频信号具有良好的滤波性能，对高频信号提高了跟踪能力。
