陀螺漂移的状态空间建模方法

State space modeling of random drift rate in gyro

非平稳时间序列的状态空间建模技术被用于陀螺漂移分析。基于平滑先验概念，使用Ｋａｌｍａｎ滤波和ＡＩＣ准则拟合整体模型，时变系数自回归模型（ＡＲ）被拟合为状态空间模型，并且被用于时变谱估计，针对两类不同的漂移分别建立状态空间模型，所得结果更贴近实际情况，目前本计算分析只能离线分。对故障诊断可提供一些依据。

? A state space approach for the modeling of nonstationary time series is presented. Based on the concept of smoothness priors constraint, the overall model is fitted by using the Kalman filter and Akaike's AIC criteion. Whenever an autoregressive(AR) model with time changing coefficient is fitted in state space model, it can be used for the time changing spectrum estimation. Some numerical results of gyro drift models are obtained for analysis of a gyro. As the trend, irreguiar and periodic components of the observed time series can be modeled simultaneously, it is statistically more accurate and efficient than that modeled separately. 