北京航空航天大学学报 ›› 2010, Vol. 36 ›› Issue (6): 636-639.

• 论文 • 上一篇    下一篇

MEMS陀螺仪随机漂移仿真和试验

  

  1. (哈尔滨工程大学 自动化学院, 哈尔滨 150001)
  • 收稿日期:2009-04-17 出版日期:2010-06-30 发布日期:2010-07-02
  • 作者简介:钱华明(1965-),男,安徽池州人,教授, qianhuam@sina.com.

Simulation and experiment of random errors of MEMS gyroscope

  • Received:2009-04-17 Online:2010-06-30 Published:2010-07-02

摘要:

为了提高使用精度,研究了某微机电系统(MEMS, Micro Electro Mechanical System)陀螺仪的随机漂移模型.应用时间序列分析方法对经过预处理的陀螺仪量测数据进行建模,提出采用状态扩增法设计Kalman滤波器.进行速率试验和摇摆试验,验证了在静态和恒定角速率条件下,滤波后的误差均值和标准差分别为滤波前的55%和12%.针对在摇摆运动时随着振幅的增加滤波效果下降的问题,设计了自适应Kalman滤波器,分析了衰减因子的选取原则.仿真结果表明:常值衰减因子法和自适应衰减因子法都能显著改善摇摆运动时的滤波效果,而自适应衰减因子法的精度更高.

Abstract:

The random errors of a micro electro mechanical system (MEMS) gyroscope was analyzed and modeled to improve gyroscope performance. Time series analysis was used to fit the gyroscope measurement data which had been preprocessed. State vector augmenting method was proposed to design Kalman filter. In order to verify the validity of the method, rate test and oscillating test had been done. After filtering, in the case of static and constant angular rate, the mean value and standard deviation were 55% and 12% of that before filtering respectively. However, the effect decreased when it turns to oscillating environment. Adaptive Kalman filter was adopted to solve the problem. The choosing principle of fading factor was discussed and the filtering performance of constant fading factor was compared with that of adaptive factor. The results showed that, in the case of oscillating, both of them could get a remarkable performance improvement, and the filtering performance of the adaptive fading factor is higher than that of the constant one.

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