Feng Lihui, Du Le, Guo Junqiang, Cui Jianmin, Lu Jihua, Zhu Zhengqiang, Wang Lijuan
School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Key Laboratory of Photonics Information Technology, Ministry of Industry and Information, Beijing 100081, China.
Micromachines (Basel). 2022 Dec 30;14(1):109. doi: 10.3390/mi14010109.
The applications of Micro-Electro-Mechanical-System (MEMS) gyros in inertial navigation system is gradually increasing. However, the random drift of gyro deteriorates the system performance which restricting the applications of high precision. We propose a bias drift compensation model based on two-fold Interpolated Complementary Ensemble Local Mean Decomposition (ICELMD) and autoregressive moving average-Kalman filtering (ARMA-KF). We modify CELMD into ICELMD, which is less complicated and overcomes the endpoint effect. Further, the ICELMD is combined with ARMA-KF to separate and simplify the preprocessed signal, resulting improved denoising performance. In the model, the abnormal noise is removed in preprocess by 2σ criterion with ICELMD. Then, continuous mean square error (CMSE) and Permutation Entropy (PE) are both applied to categorize the preprocessed signal into noise, mixed and useful components. After abandon the noise components and denoise the mixed components by ARMA-KF, we rebuild the noise suppression signal of MEMS gyro. Experiments are carried out to validate the proposed algorithm. The angle random walk of gyro decreases from 2.4156∘/h to 0.0487∘/h, the zero bias instability lowered from 0.3753∘/ to 0.0509∘/. Further, the standard deviation and the variance are greatly reduced, indicating that the proposed method has better suppression effect, stability and adaptability.
微机电系统（MEMS）陀螺仪在惯性导航系统中的应用正在逐渐增加。然而，陀螺仪的随机漂移会降低系统性能，从而限制了其在高精度领域的应用。我们提出了一种基于双重插值互补总体局部均值分解（ICELMD）和自回归移动平均 - 卡尔曼滤波（ARMA - KF）的偏差漂移补偿模型。我们将CELMD修改为ICELMD，它更简单且克服了端点效应。此外，将ICELMD与ARMA - KF相结合，对预处理后的信号进行分离和简化，从而提高去噪性能。在该模型中，通过ICELMD在预处理阶段利用2σ准则去除异常噪声。然后，应用连续均方误差（CMSE）和排列熵（PE）将预处理后的信号分类为噪声、混合和有用成分。在舍弃噪声成分并通过ARMA - KF对混合成分进行去噪后，我们重建了MEMS陀螺仪的噪声抑制信号。通过实验验证了所提出的算法。陀螺仪的角度随机游走从2.4156°/h降至0.0487°/h，零偏不稳定性从0.3753°/降至0.0509°/。此外，标准差和方差大幅降低，表明所提出的方法具有更好的抑制效果、稳定性和适应性。
