Department of Artillery Engineering, Army Engineering University of PLA, Shijiazhuang 050003, China.
School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Sensors (Basel). 2022 Jul 13;22(14):5225. doi: 10.3390/s22145225.
Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s to -1.2646 × 10 m/s, while the variance decreases from 0.0133 m/s to 1.0975 × 10 m/s. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.
随机漂移误差是 MEMS（微机电系统）传感器输出误差的重要因素之一。识别和补偿传感器输出误差是提高传感器精度的重要手段。为了降低白噪声对神经网络建模的影响，使用集合经验模态分解（EEMD）方法将白噪声从原始信号中分离出来。去除噪声后的漂移信号通过 GRNN（广义回归神经网络）进行建模。为了达到更好的建模效果，设计了交叉验证和参数优化算法来获得最优的 GRNN 模型。该算法用于对生成的随机漂移信号进行建模和补偿误差。结果表明，原始信号的均值从 0.1130m/s 降低到-1.2646×10m/s，而方差从 0.0133m/s 降低到 1.0975×10m/s。此外，通过 MEMS 加速度计进行了位移测试。实验结果表明，通过补偿 MEMS 传感器的输出误差，位移测量精度从 95.64%提高到 98.00%。通过与 GA-BP（遗传算法-反向传播）神经网络和多项式拟合方法进行比较，本文提出的 EEMD-GRNN 方法可以有效地识别和补偿复杂的非线性漂移信号。
