基于Kalman光纤陀螺的随机信号处理

Based on Kalman Filter Processing of FOG Signal

分析了光纤陀螺随机漂移的特性,建立了随机漂移的ARIMA模型;针对IFOG输出信号的特点建立了随机漂移的观测方程并用卡尔曼滤波把随机漂移估计出来,用以对IFOG的输出进行补偿。根据时间序列预测的特点和要求,分析了传统时间序列预测方法的不足,提出了将卡尔曼滤波应用于时间序列预测。推导了基于卡尔曼滤波的ARMA模型参数实时更新算法,并采用功率谱密度分析方法确定预测模型的形式与阶数;通过对光纤陀螺随机漂移建模进行了实证研究,对光纤陀螺仪信号处理有着重要的参考。

Considering the characteristics and requirements of fault forecast, the deficiencies of traditional forecast methods were pointed out, the method that apply Kalman Filter to time series forecasting was put forward. The method for estimating parameters of ARMA（autoregressive moving average） based on Kalmau Filter was deducted, the component of modeling was analysis with PSD（ power spectrum density）. The theory above was demonstrated though the modeling of random drift for FOG（fiber optic gyro）.