基于l_2范数的EMD滤波方法在光纤陀螺信号中的应用（英文）

kApplication of EMD filtering based on l_2-norm in denoising FOG signal

光纤陀螺的随机漂移限制了惯性导航系统的精度,如何减小它是一项非常艰巨的任务。结合经验模态分解(EMD)和信号与模态之间的概率密度函数,提出了一种新型的依赖Hurst指数的信号滤波方法。当H<0.5时,利用l_2范数选择出相关模态,累加并形成的部分重构方法来对光纤陀螺的信号进行滤波;当H≥0.5时,间隔阈值的经验模态(EMD-IT)被引入对相关模态进行滤波,之后按照部分重构的方法对光纤陀螺的信号进行滤波;称为混合的EMD-pdf和EMD-IT。与其它的滤波方法进行对比,如基于相关函数的EMD部分重构(EMD-cor),基于概率密度函数的EMD部分重构(EMD-pdf),仿真信号和实际数据结果表明,该混合模型的优越性,有效减小了光纤陀螺的随机误差。

To reduce the random noise and drift of fiber optic gyros（FOG）, a novel Hurst-exponent-based signal filtering method is introduced, which combines the empirical mode decomposition（EMD） and the similarity measure between the probability density function（pdf） of the signal and the pdf of each mode. When H0.5, the relevant modes are selected by using l_2-norm, and the partial reconstruction with relevant modes is formed and used to filter the FOG＇s noisy signals; When H≥ 0.5, EMD interval thresholding（EMD-IT） is introduced into the relevant modes to carry on filter processing, then the FOG＇s noisy signals are filtered according to partial reconstruction approach. This method is called as mixed EMD-pdf EMD-IT method. Simulation and real data test are carried out to evaluate the effectiveness of the proposed method. Other EMD denoising methods, such as correlation-based EMD reconstruction（EMD-cor）, single pdf-based EMD reconstruction（EMD-pdf）, are also investigated to provide a comparison with this method. Experiment results show that its performance is superior to those of the other EMD denoising methods.