摘要
针对MEMS陀螺仪受随机误差影响较大需要进行滤波处理,采用时间序列分析法建立的随机误差模型无法直接用于动态条件下滤波的问题,提出了一种基于角速度估计的随机误差动态滤波方法。首先,采用时间序列分析法对MEMS陀螺仪随机误差进行分析与模型构建;然后,将角速度估计假设模型建模为三维线性模型,并与陀螺仪随机误差模型结合构建动态滤波模型;最后,采用强跟踪卡尔曼滤波方法直接估计出角速度值以实现对随机误差滤波,并进行试验验证。结果表明:无论是静态还是动态条件下,该滤波方法估计的角速度值精度均较高,可以有效降低MEMS陀螺仪的随机误差,提升MEMS陀螺仪精度。
To solve the problems that the MEMS gyroscope is greatly affected by random error and filtering is needed and the random error model established by time series analysis method cannot be directly applied to the dynamic filtering a random error dynamic filtering method based on angular velocity estimation model is proposed.Firstly analysis is made to the MEMS gyroscope random error and the model is established by using time series analysis method.Then the angular velocity estimation hypothesis model is established as a three-dimensional linear model which is combined with gyroscope random error model to form a dynamic filtering model.Finally strong tracking Kalman filter method is used directly to estimate the angular velocity value for realizing random error filtering and experimental verification is made.The results show that:The angular velocity value estimation accuracy of this filtering method is high under both static and dynamic conditions and it can effectively reduce the random error of MEMS gyroscope and improve its accuracy.
作者
刘文超
郑小兵
王荣颖
李曦
LIU Wenchao;ZHENG Xiaobing;WANG Rongying;LI Xi(No.91550 Unit of PLA Dalian,116023 China;Naval University of Engineering,Wuhan 430033 China)
出处
《电光与控制》
CSCD
北大核心
2021年第5期79-84,共6页
Electronics Optics & Control
基金
国家自然科学基金(41506220)。
