TANG Xiao-hong, ZHAO Lu-yang, LI Lu-ming, et al. Modified adaptive filtering algorithm for MEMS gyroscope random noise[J]. 2018, (10): 133-136. DOI: 10.13873/j.1000-9787(2018)10-0133-04.
改进的MEMS陀螺随机噪声自适应滤波算法
摘要
针对微机电系统(MEMS)陀螺仪随机噪声建模时
自回归滑动平均(ARMA)模型中有色噪声白化问题
提出了将有色噪声作为系统状态方程控制项
以改进解耦自适应Kalman滤波算法
从而提高MEMS陀螺仪的噪声补偿效果。实验结果表明:经改进算法滤波后
MEMS陀螺仪噪声均方差减小85.1%
随机误差项减小41.9%;与标准Kalman滤波算法、解耦自适应Kalman滤波算法相比
改进算法的随机误差项分别减小30.5%
10.1%
能更好地降低MEMS陀螺仪随机噪声。
Abstract
Aiming at the problem of whitening of colored noise in auto-regressive moving average( ARMA) model while modeling the micro-electro-mechanical system( MEMS) gyroscope random noise
colored noise is used as control term of system state equation to modify decoupling adaptive Kalman filtering algorithm
and the effect of noise compensation of MEMS gyro is improved. Experimental results show that after modified algorithm filtering
mean square deviation of noise is reduced by 85. 1 % and random noise term is reduced by 41. 9 %. Compared with standard Kalman filtering algorithm
decoupling adaptive Kalman filtering algorithm
random noise is reduced by 30. 5 %
and 10. 1 %
separately
and can reduce random noise of MEMS gyroscope more efficiently.