基于Kalman滤波的Allan方差参数实时自主估计

针对目前研制高精度光纤陀螺仪(FOG)过程中精度提高很大程度受到了随机过程噪声的影响,特别是环境温度变化对光纤陀螺的随机噪声误差项的影响等问题,本文提出了基于指数加权移动平均算法和非线性自适应Kalman滤波器的光纤陀螺Alian方差参数实时自主估计方法。通过运用非线性自适应Kalnan滤波器实时估计常温条件下光纤陀螺的Allan方差参数和变温条件下的Allan方差参数,为后续光纤陀螺噪声抑制补偿及精度提高提供了一种新的方法。通过分别对常温条件和变温条件下光纤陀螺静态实验数据研讨,表明所提出的方法能很好地进行光纤陀螺Allan方差参数实时自主估计,为光纤陀螺噪声误差补偿奠定了基础。

Allan Variance Coefficients Real-time Autonomous Estimation based on Kalman Filter

According to the situations that the accuracy improvement in developing fiber optic gyro （FOG） degraded by stochastic process noise greatly, especially the varied environment temperature has great influence on FOG stochastic noise items. This paper proposed exponential weighting moving average algorithm and online autonomous estimation method for FOG Allan variance coefficients with nonlinear adaptive Kalman filter on FOG Allan variance coefficients in constant temperature and varied temperature conditions, which supplied a new way for FOG noises constrain compensation and accuracy improvement. Experiments data of constant temperature and varied temperature analysis show that the propose methods could make online autonomous estimation of FOG Allan variance coefficients properly and make the foundation of FOG noise errors online compensations.