基于改进型经验模分解的陀螺漂移趋势提取

非线性,非平稳的时间序列经过经验模分解,可以得到一组内模函数和一个基本的趋势项。本文针对陀螺弱非线性、非平稳漂移时间序列的趋势项提取这一问题,引入了改进的经验模分解方法。该方法通过二拍自适应滤波对原经验模分解中局部均值的估计方法进行了改进,并利用自适应边界估值来抑制其边缘效应。给出了应用该方法的具体步骤,并对一组陀螺漂移序列进行了实际处理。实验结果表明,该方法有效提取了陀螺的漂移趋势项。

Trend extraction of gyro's drift based on modified empirical mode decomposition

Nonlinear and nonstationary time series are decomposed into a series of instrinsic mode functions and a residual trend item by the empirical mode decomposition (EMD). A modified EMD method to extract the trend item of Gyro's drifts is introduced. The method modifies local mean estimation method of EMD by the two-tap adaptive time-varying filter, and restrains end effects using the adaptive border estimation. The concrete steps of the proposed method are presented and applied to a Gyro's drift time series. The experiment result indicates this method is efficacious.