Wang Xinwang, Cui Ying, Cao Huiliang
School of Instrument Science and Engineering, Southeast University, Nanjing 210018, China.
School of Automotive and Transportation, Wuxi Institute of Technology, Wuxi 214000, China.
Micromachines (Basel). 2023 Aug 31;14(9):1712. doi: 10.3390/mi14091712.
This study proposes an improved multi-scale permutation entropy complete ensemble empirical mode decomposition with adaptive noise (MPE-CEEMDAN) method based on adaptive Kalman filter (AKF) and grey wolf optimizer-least squares support vector machine (GWO-LSSVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and a gyro output signal is obtained with better accuracy. Firstly, MPE-CEEMDAN is used to decompose the FOG output signal into several intrinsic mode functions (IMFs); then, the IMFs signal is divided into mixed noise, temperature drift, and other noise according to different frequencies. Secondly, the AKF method is used to denoise the mixed noise. Thirdly, in order to denoise the temperature drift, the fiber gyroscope temperature compensation model is established based on GWO-LSSVM, and the signal without temperature drift is obtained. Finally, the processed mixed noise, the processed temperature drift, the processed other noise, and the signal-dominated IMFs are reconstructed to acquire the improved output signal. The experimental results show that, by using the improved method, the output of a fiber optic gyroscope (FOG) ranging from -30 °C to 60 °C decreases, and the temperature drift dramatically declines. The factor of quantization noise (Q) reduces from 6.1269 × 10 to 1.0132 × 10, the factor of bias instability (B) reduces from 1.53 × 10 to 1 × 10, and the factor of random walk of angular velocity (N) reduces from 7.8034 × 10 to 7.2110 × 10. The improved algorithm can be adopted to denoise the output signal of the FOG with higher accuracy.
本研究提出了一种基于自适应卡尔曼滤波器（AKF）和灰狼优化器-最小二乘支持向量机（GWO-LSSVM）的改进型多尺度排列熵完全集成经验模态分解自适应噪声（MPE-CEEMDAN）方法。通过建立温度补偿模型，对陀螺温度输出信号进行优化和重构，得到精度更高的陀螺输出信号。首先，利用MPE-CEEMDAN将光纤陀螺（FOG）输出信号分解为多个本征模态函数（IMF）；然后，根据不同频率将IMF信号分为混合噪声、温度漂移和其他噪声。其次，采用AKF方法对混合噪声进行去噪。第三，为了消除温度漂移，基于GWO-LSSVM建立光纤陀螺温度补偿模型，得到无温度漂移的信号。最后，对处理后的混合噪声、处理后的温度漂移、处理后的其他噪声以及以信号为主的IMF进行重构，得到改进后的输出信号。实验结果表明，采用改进方法后，光纤陀螺（FOG）在-30℃至60℃范围内的输出降低，温度漂移显著下降。量化噪声（Q）因子从6.1269×10降至1.0132×10，偏置不稳定性（B）因子从1.53×10降至1×10，角速度随机游走（N）因子从7.8034×10降至7.2110×10。该改进算法可用于对光纤陀螺输出信号进行更高精度的去噪。
