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摘要:
为了抑制微机械电子系统（MEMS）陀螺仪的随机漂移，基于经验模态分解（EMD）和模态集合选择标准，结合时间序列建模滤波法，提出了一种改进的MEMS陀螺仪随机漂移分析方法。首先，通过EMD将MEMS陀螺仪原始数据分解为多个本征模态函数（IMF），利用模态集合选择标准将IMF分为噪声IMF、噪声与信号混合IMF和信号IMF三类；然后，对混合IMF进行重构、时间序列建模及自适应卡尔曼滤波（AKF）；最后，将3类信号重构，实现MEMS陀螺仪信号去噪。实验表明：所提方法有更好的去噪效果和实时性，提高了MEMS陀螺仪的使用精度。
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关键词:
- 微机械电子系统(MEMS)陀螺仪 /
- 自适应卡尔曼滤波(AKF) /
- 时间序列模型 /
- Allan方差 /
- 经验模态分解(EMD)
Abstract:In order to reduce the random drift of Micro-Electro-Mechanical System (MEMS) gyroscope, an improved random drift analysis method of MEMS gyroscope is proposed, based on an improved Empirical Mode Decomposition (EMD) and a mode set selection criterion, combined with the method of time series model and filter. The original data of MEMS gyroscope was decomposed into several Intrinsic Mode Functions (IMFs) by EMD, and IMFs were divided into noise IMFs, mixed IMFs and signal IMFs by using the mode set selection criterion. The mixed IMFs were reconstructed, the time series model of the mixed IMFs after reconstruction was formulated, and Adaptive Kalman Filter (AKF) after modeling was finished. The denoised signal is obtained by reconstruction of three types of signal. Experimental result shows that the proposed method has better denoising effect and real-time performance, which greatly improves the using precision of MEMS gyroscope.
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表 1 相关程度判断标准
Table 1. Judgment criteria of correlation degree
相关系数 相关程度 0.8~1.0 极强相关 0.6~0.8 强相关 0.4~0.6 中度相关 0.2~0.4 弱相关 0~0.2 极弱相关或无相关 
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表 2 各阶IMF分量的相关系数值
Table 2. Correlation coefficient of each order of IMF
IMF 相关系数 IMF 相关系数 1 0.961 8 6 0.011 4 2 0.256 8 7 0.006 4 3 0.077 8 8 0.003 8 4 0.032 2 9 0.002 5 5 0.014 5 10 0.002 9
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表 3 各阶IMF自相关函数方差
Table 3. Variance of autocorrelation function of each order of IMF
IMF 方差 IMF 方差 1 3.13×10-5 6 4.82×10-4 2 2.72×10-5 7 0.001 0 3 5.20×10-5 8 0.001 9 4 1.09×10-4 9 0.004 5 5 2.24×10-4 10 0.006 1
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表 4 AR模型系数
Table 4. Coefficientof AR model
模型 a1 a2 a3 FPE AIC AR(1) -0.935 1 0 0 3.693 6×10-4 -5.065 9×105 AR(2) -1.750 0 0.870 8 0 8.927 3×10-5 -6.485 9×105 AR(3) -2.353 0 2.083 3 -0.692 0 4.644 4×10-5 -7.139 4×105
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表 5 Allan方差误差项对比分析
Table 5. Comparison analysis on Allan variance error terms
原始数据和方法 量化噪声/(°) 角度随机游走/ ((°)·h 
零偏不稳定性/ ((°)·h-1) 角速率游走/ ((°)·h 
速率斜坡/ ((°)·h-2) 原始数据 77.996 1 1.371 4 95.828 5 261.649 9 203.768 0 KF 26.262 5 1.394 9 36.813 8 100.523 7 78.284 4 EMD-KF 6.527 1 0.577 5 46.413 7 122.321 9 94.460 9 改进EMD-AKF 3.422 5 0.302 7 29.132 0 71.074 3 53.691 5
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