一种光纤陀螺随机噪声时间序列建模与实时滤波方法

胡俊伟; 刘明雍; 张加全

doi:10.11993/j.issn.1673-1948.2011.01.007

一种光纤陀螺随机噪声时间序列建模与实时滤波方法

doi: 10.11993/j.issn.1673-1948.2011.01.007

西北工业大学航海学院, 陕西西安, 710072

基金项目: 国家自然科学基金(50979093)，新世纪优秀人才计划资助(NCET-06-0877)

详细信息

作者简介:
胡俊伟(1985-), 男, 在读硕士, 研究方向为水下航行器惯性导航与制导.

中图分类号: TJ630.33
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- 文章访问数: 1264
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- 被引次数: 0
出版历程
- 收稿日期: 2010-05-20
- 修回日期: 2010-07-05
- 刊出日期: 2011-02-28

A Time Series Modeling and Real Time Filtering Method for FOG Random Noise

College of Marine Engineering, Northwestern Polytechnical University, Xi'an 710072, China

摘要

摘要: 为了减小光纤陀螺(FOG)的随机噪声, 利用时间序列分析法对FOG的随机噪声进行了分析与建模, 并在建立的自回归滑动平均(ARMA(2,1)）模型基础上, 采用一种将改进递推增广最小二乘(RELS)算法和Sage-Husa自适应卡尔曼滤波算法相结合的方法, 对采集的FOG静态输出随机噪声进行实时补偿, 同时与标准kalman滤波算法进行仿真对比。仿真结果表明, 该方法具有更好的补偿效果, 可更有效地抑制FOG随机噪声。
- 光纤陀螺(FOG) /
- 自回归滑动平均(ARMA)模型 /
- 递推增广最小二乘法(RELS) /
- Sage-Husa自适应卡尔曼滤波
Abstract: In order to reduce the random noise of fiber optic gyroscope (FOG), the random noise of FOG is analyzed and modeled by time series analysis method. Based on the established auto regression moving average（ARMA）(2, 1) model, a method that combines modified recursive extended least squares (RELS) algorithm and Sage-Husa adaptive Kalman filter algorithm is proposed to compensate the static output random noise of FOG in real-time. In addition, the standard Kalman filter algorithm is used for comparison. Simulation results show that the proposed method achieves better com-pensation effect and more effective inhibition of the FOG random noise.
- fiber optic gyroscope(FOG) /
- auto regression moving average(ARMA) model /
- recursive extended least squares(RELS) /
- Sage-Husa adaptive Kalman filter

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参考文献(8)

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