Applicability compensation method for random drift of fiber optic gyroscopes
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摘要: 针对传统方法在建立时间序列模型基础上应用卡尔曼滤波器去除陀螺随机噪声误差的缺陷,提出了一种适于在线补偿光纤陀螺FOG(Fiber Optic Gyrosope)随机误差的滤波方法.当建立的时间序列模型系数出现偏差时,通过引入虚拟噪声,来补偿滤波过程由于系统模型时变和未知噪声而引入的误差,实现了对陀螺随机漂移误差的高精度滤波处理.其次,利用Allan 方差分析法分离并确定了光纤陀螺的主要随机误差源,并对建立的光纤陀螺时间序列模型及滤波方法的适用性及精度进行了评估.通过对光纤陀螺实测数据的分析表明,速率斜坡、速率随机游走和零偏稳定性为FOG的主要随机噪声,所提出的自适应滤波算法能够适应陀螺漂移的时变特点,是一种有效的去除光纤陀螺随机漂移噪声方法.Abstract: Based on the limitation of founding a time-sequence model and using Kalman filter to wipe off the random noise, an adaptive Kalman filtering arithmetic for on-line compensation of the random errors of fiber optic gyroscope (FOG) was presented. As emerging error in a time-sequence model, the time-varied model and unknown noise can be compensated by introducing a pseudo noise.Consequently, an appropriate high-precision filter was designed for the FOG inertial navigation system. The Allan variance was utilized to analyze the output of FOG, and some main noise of FOG can be extracted and confirmed, then the performance and precision of the time-sequence model of FOG can be evaluated. The practical data of FOG was analyzed. It is shown that the main random noise of FOG are the angular rate ramp, the rate random walk and the bias instability, and the proposed adapted Kalman filter arithmetic can adapt to the time-varied characteristic of the FOG drift, and it is an effective method for FOG to wipe off it-s random drift noise.
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Key words:
- fiber optic sensors /
- random errors /
- Kalman filtering /
- model structures
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