Modeling and identification of random drift for FOG
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摘要: 随机漂移是光纤陀螺的主要误差,建立数学模型在输出中补偿是抑制该项误差、提高光纤陀螺精度的有效方法.光纤陀螺静态输出为随机过程,对该随机过程的平稳性和正态性进行分析,拟合趋势项、周期项并补偿,使其成为平稳随机序列.采用时间序列分析法建立光纤陀螺随机漂移模型,根据随机漂移自相关和偏相关系数的特性辨识模型的类型和阶数,利用最小二乘方法估计模型参数,得到光纤陀螺随机漂移模型为AR(2).对陀螺输出数据补偿,检验模型的适用性.结果表明,该模型具有很好的适用性,能够有效抑制随机漂移,提高光纤陀螺精度,可以作为惯导系统卡尔曼滤波器状态变量的数学模型.Abstract: Random drift is a main error of fiber optic gyro (FOG), it is a efficient method to reduce the random drift and improve the accuracy by modeling and compensation from the output of FOG. The static output of FOG is a random project, the steability and randomicity are analysised, the trend and periodicity is suited and compensated. The random drift of FOG is modeled by time series analytical method, the type and ladder are identificied by the autocorrelation function. The coefficient of the model is estimated by the least square, the FOG random drift model is AR(2). The raw data is compensated with the model, and the validity of the model is tested .It is shown that the result is greatly improved, and the random drift is efficiently reduced, the accuracy of the FOG is greatly improved. The model also is as the state of Kalman filter in the inertial navigation system.
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Key words:
- fiber optic gyro(FOG) /
- random drift /
- error modeling
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