光纤陀螺随机漂移的建模与滤波研究  

Study on Modeling and Filtering of the Fiber Optic Gyroscope Random Drift

作　　者：段苛苛[1] 李邓化[2] 伊丽娜[2] 

机构地区：[1]北京交通大学电子信息工程学院,北京100044 [2]北京信息科技大学自动化学院,北京100101

出　　处：《压电与声光》2011年第5期738-741,共4页Piezoelectrics & Acoustooptics

基　　金：北京市属市管高等学校人才强教计划基金资助项目(PHR200907124)

摘　　要：时间序列模型是对光纤陀螺(FOG)随机漂移进行建模的一种重要方法。传统的时间序列建模方法难以应用于实时建模,且模型精度较低。因此,提出了适用于高精度FOG随机漂移的改进自回归整合移动平均模型(ARIMA),并基于该模型建立了FOG随机漂移的实时Kalman滤波器。实验结果表明,该改进ARIMA模型能较准确地描述FOG的随机漂移;Allan方差分析结果表明,与基于传统自回归移动平均模型(ARMA)的Kalman滤波结果相比,基于该模型的Kalman滤波对减小光纤陀螺的5项主要随机误差更有效。The time series model is an important modeling approach of the FOG random drift. The conventional time series modeling approach is difficult to be applied in real-time condition, and has the feature of low accuracy. To overcome these problems, an improved Autoregressive Integrated Moving Average （ARIMA） model which can be applied to the modeling of high-precision FOG＇s random drift has been presented, and the real time Kalman filter has been fabricated based on the model. The experimental results indicated that the improved ARIMA model was able to describe the random drift of FOG quite accurately; Allan variance analysis indicated that, comparing with the Kalman filter based on the Auto-Regressive and Moving Average （ARMA） model, the Kalman filter based on the improved ARIMA model had reduced the main random drift error of FOG more efficiently.

关 键 词：光纤陀螺 随机漂移 ALLAN方差 平稳性检验 KALMAN滤波 

分 类 号：V241.5[航空宇航科学与技术—飞行器设计]