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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 method of modeling FOG random drift. The traditional time series modeling method is difficult to apply to real-time modeling, and the model accuracy is low. Therefore, an improved autoregressive integrated moving average (ARIMA) model is proposed for high-precision FOG random drift and a real-time Kalman filter based on FOG random drift is proposed. The experimental results show that the improved ARIMA model can describe the random drift of FOG more accurately. The Allan variance analysis shows that compared with the Kalman filter based on the traditional autoregressive moving average (ARMA) model, the Kalman filter based on the model reduces Five major random errors in a small fiber optic gyroscope are more efficient.