基于静态Allan方差分析方法无法有效分析和辨识动态工况下激光陀螺仪的随机误差,也无法给动态工况下激光陀螺仪的随机误差补偿提供准确依据。为此,提出时间框动态Allan方差分析方法,利用分段建模对随机误差项进行动态Allan方差分析和辨识。建立灰色GM(1,1)预测模型,对辨识出的随机误差参数项进行预测,针对传统GM(1,1)预测模型因数据不全存在波动大的问题,基于小波滤波平滑处理原始数据,并利用残差修正模型改进GM(1,1)预测模型。实验结果表明,针对激光陀螺仪在同一工况下的随机误差系数,改进GM(1,1)模型预测算法的预测精度高于传统GM(1,1)模型预测算法的预测精度。

The static Allan analysis of variance cannot effectively analyze and identify the random error of a laser gyroscope under static conditions. Further, it cannot provide an accurate basis for the random error compensation of a laser gyroscope under dynamic conditions. Therefore, in this study, we propose a time-frame dynamic Allan analysis of variance method to conduct dynamic Allan analysis of variance and identify the random error terms via piecewise modeling. The grey GM (1, 1) prediction model is established to identify the random error associated with the parameters that have to be predicted. However, the problem of large fluctuation can be observed with respect to the data from the traditional GM (1,1) prediction model is not complete; therefore, we introduce wavelet filters to smoothen the original data and the residual error correction model to improve the GM (1,1) prediction model. The test results denote that the prediction accuracy of the improved GM (1, 1) prediction model is higher than that of the traditional GM (1,1) prediction model for the random error coefficients of laser gyro under the same working condition.