基于EMD的MEMS陀螺仪随机漂移分析方法  

Random drift analysis method of MEMS gyroscope based on EMD

作　　者：李文华 汪立新 沈强 李成 LI Wenhua;WANG Lixin;SHEN Qiang;LI Cheng(College of Missile Engineering,Rocket Force Engineering University,Xi'an 710025,China)

机构地区：[1]火箭军工程大学导弹工程学院,西安710025

出　　处：《北京航空航天大学学报》2021年第9期1927-1932,共6页Journal of Beijing University of Aeronautics and Astronautics

基　　金：陕西省自然科学基础研究计划(2020JQ-491)。

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。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.

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) 

分 类 号：V241.5[航空宇航科学与技术—飞行器设计]