北京航空航天大学学报 ›› 2009, Vol. 35 ›› Issue (2): 246-250.

• 论文 • 上一篇    下一篇

MEMS陀螺仪随机误差滤波

陈殿生1, 邵志浩1, 雷旭升2, 王田苗1   

  1. 1. 北京航空航天大学 ]机械工程及自动化学院, 北京 100191;
    2. 北京航空航天大学 仪器科学与光电工程学院, 北京 100191
  • 收稿日期:2008-07-20 出版日期:2009-02-28 发布日期:2010-09-16
  • 作者简介:陈殿生(1969-),男,吉林扶余人,教授,chends@163.com.
  • 基金资助:

    国家杰出青年科学基金资助项目(60525314);国家863计划资助项目(2007AA04Z250,2006AA04Z206);国际科技合作资助项目(2008DFR70100)

Multiscale fyzzy-adaptive Kalman filtering methods for MEMS gyros random drift

Chen Diansheng1, Shao Zhihao1, Lei Xusheng2, Wang Tianmiao1   

  1. 1. School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
    2. School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2008-07-20 Online:2009-02-28 Published:2010-09-16

摘要: 针对微机电系统(MEMS,Micro Electromechanical System)陀螺仪的随机漂移,基于小波多尺度分析,利用bior1.5小波对陀螺仪的随机漂移进行深度为4的分解,重建各尺度信号,采用时间序列方法对陀螺仪各尺度随机漂移进行建模,与传统时间序列方法建模相比,降低了模型的预测误差.并构建了模糊自适应Kalman滤波,利用模糊控制方法基于残差均值与方差差值对噪声方差阵进行实时调整,提高对重建后的各尺度信号随机噪声滤波效果.通过一系列对比实验证明,基于多尺度分析的模糊自适应Kalman滤波对于消除MEMS陀螺仪随机漂移误差作用明显.通过Allan方差分析,滤波后的数据各随机误差项均得到有效减小.

Abstract: A new time series method was proposed to construct the random drift model for the micro electro mechanical sensor (MEMS) gyro. Based on the wavelet multi scale analysis method, the gyro random drift data was decomposed to a series of scale gyro drift data with depth of 4 using bior1.5 wavelet, each scale signal was rebuilt and then constructed the corresponding multi scale time series models to reduce the overall predict error. Moreover, an adaptive Kalman filter algorithm was proposed to improve the compensation performance for the random drift noise. The noise variance was modified by using the fuzzy adaptive system which is based on the mean and variance margin of residual sequence. The effectiveness of the proposed method was proved by a series of experiments compared with multi scale analysis with simple Kalman filter (SKF). Each random item was reduced using Allan variance analysis.

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