针对半球谐振陀螺仪的输出可能不符合时间序列分析的平稳性要求的问题, 提出了一种基于经验模态分解的改进时间序列灰色预测方法,该方法将经验模态分解、时间序列建模和灰色预测结合起来。首先利用经验模态分解对陀螺仪的输出原始信号进行预处理, 分解得到原始信号中包含的随机项和趋势项, 然后对这些信号进行平稳性检验, 根据检验结果选择时间序列分析和灰色预测对这些数据分别进行建模预测, 最后将预测结果进行重构得到最终的预测值。仿真实验结果表明,该方法的预测效果比单独使用时间序列分析进行建模预测的效果要好。

In order to solve the problem that HRGs output may not satisfy the stability requirement of time series analysis an improved time series-grey prediction method based on empirical mode decomposition was proposed.This method combined empirical mode decomposition with time series modeling and grey prediction.First the empirical mode decomposition was used for preprocessing the original signals of the gyro and separating the trend part from random part.Then the stability of the signals was analyzed and time series analysis and grey prediction were used to model and predict the data according to the result of analysis.At last the final prediction result was obtained by reconstructing the prediction result.Simulation results demonstrate that the prediction effect of this method is better than the one using time series analysis alone.