MEMS陀螺仪随机误差滤波

陈殿生; 邵志浩; 雷旭升; 王田苗

MEMS陀螺仪随机误差滤波

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

基金项目: 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)

详细信息

作者简介:
陈殿生(1969-),男,吉林扶余人,教授,chends@163.com.

中图分类号: TP 242
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- 被引次数: 0
出版历程
- 收稿日期: 2008-07-20
- 网络出版日期: 2009-02-28

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

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

摘要

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.
- 陀螺仪 /
- 随机误差 /
- 小波分析 /
- Kalman滤波 /
- 模糊控制
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.
- gyroscopes /
- random errors /
- wavelet analysis /
- Kalman filter /
- fuzzy control

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参考文献(1)

[1] 张海鹏,房建成．MEMS陀螺仪短时漂移特性实验研究[J]．中国惯性技术学报,2007, 15(1):100-104 Zhang Haipeng, Fang Jiancheng. Short-time drift characteristic of MEMS gyroscope[J]. Journal of Chinese Inertial Technology, 2007, 15(1):100-104(in Chinese) [2] 徐丽娜,邓正隆.陀螺仪漂移特性的小波分析[J].中国惯性技术学报,2001,9(3):57-60 Xu Lina, Deng Zhenglong. Wavelet analysis on gyro drift rate[J]. Journal of Chinese Inertial Technology, 2001, 9(3):57-60(in Chinese) [3] 赵世峰,张海,沈小蓉,等． MEMS陀螺随机噪声的多尺度时间序列建模[J]．中国惯性技术学报,2006,14(5):78-80 Zhao Shifeng, Zhang Hai, Shen Xiaorong,et al.Modeling of MEMS gyros random noise based on multiscale timeseries[J]. Journal of Chinese Inertial Technology,2006,14(5):78-80(in Chinese) [4] 付梦印,邓志红,张继伟．Kalman滤波理论及其在导航系统中的应用[M]．北京:科学出版社,2004:92-94 Fu Mengyin, Deng Zhihong, Zhang Jiwei. Kalman filtering theory and application in navigation system[M]. Beijing: Science Press, 2004:92-94(in Chinese) [5] 刘涛,曾祥利,曾军．实用小波分析入门[M]．北京:国防工业出版社,2006:40-42 Liu Tao, Zeng Xiangli, Zeng Jun. Practical wavelet analysis[M]. Beijing: National Defense Industry Press, 2006: 40-42(in Chinese) [6] 汤巍,李士心,刘鲁源,等．关于陀螺信号处理中小波基选取的研究[J]．中国惯性技术学报,2002,10(5):28-30 Tang Wei, Li Shixin, Liu Luyuan, et al.Select of wavelet basis in gyro signal processing[J]. Journal of Chinese Inertial Technology,2002, 10(5):28-30(in Chinese) [7] 尚捷．MIMU及其与GPS组合系统设计与实验研究．北京:清华大学精密仪器与机械学系,2005 Shang Jie. Design and experimental study on MIMU and its integrated system with GPS . Beijing:Instruments Science and Technology,Tsinghua University, 2005(in Chinese) [8] Zhang Santong,Wei Xueye．Fuzzy adaptive Kalman filter for marine ins/gps navigation Proceedings of the second international conference on machine learning and cybernetics. Xi’an: IEEE, 2003:2634-2637 [9] 马野,王孝通,付建国．基于模糊卡尔曼滤波量测噪声自适应校正的方法研究[J]．中国惯性技术学报,2005,13(2):24-26 Ma Ye, Wang Xiaotong, Fu Jianguo. Adaptive adjustment based on measurement noise of fuzzy Kalman filtering[J]. Journal of Chinese Inertial Technology, 2005, 13(2):24-26(in Chinese)

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