【摘要】 经验模式分解(Empirical Mode Decomposition,EMD)是一种新的信号处理技术,它是基于数据本身的,且能在空间域中将信号进行分解,从而可以区分噪声和有用信号。根据EMD分解白噪声而得到的本征模式函数(IMF)分量的能量密度与其平均周期的乘积为一常量这一特性,建立一种新的基于EMD滤波去噪方法,并将该方法应用于GPS多路径效应的研究中。通过对模拟数据与GPS实测数据的处理分析,得出以下主要结论:①EMD滤波去噪法与小波方法都能最大限度地削弱测量的随机误差,但EMD滤波去噪法比小波方法更直接,且不受测不准原理及小波函数选择的影响;②相比小波方法,EMD能够更有效地剔除瞬时强噪声,从而能够提取更精确的多路径效应重复性误差改正模型。更多还原

【Abstract】 Empirical Mode Decomposition(EMD) is a relatively new signal processing technique.It starts from the data itself to decompose the signals in spatial domain,so it can discriminate the signals from the noise.A noise filter(EMD noise filter hereinafter) based on the characteristic of IMF(intrinsic mode function) is developed.Then,simulated data and real data(i.e.,GPS data) are used to test the method separately.The following are the conclusions drawn from these tests: ① EMD noise filter can better mitigate the random errors.Compared with wavelet method,it is more straightforward,and not subject to the effects from the selection of certain wavelet function.(② EMD) noise filter is more efficient in eliminating instantaneous strong noise than the wavelet method.It therefore can better extract and thus model the repeatability of the multipath errors,which is crucial in mitigating the errors.更多还原