GPS高程时间序列降噪分析的改进EMD方法

针对经验模态分解(empirical mode decomposition,EMD)降噪过程中不能直接确定分界本征模态函数(intrinsic mode function,IMF)的K值,以及当高频噪声IMF分量个数少于低频IMF分量个数时,利用低频信号重构实现降噪的计算量较大等问题,提出一种新的EMD降噪方法。采用平均周期与能量密度乘积指标的方法来自动确定分界IMF的K值,将高频噪声IMF分量进行重构,然后用原始信号减去重构噪声,从而达到降噪的目的。利用模拟数据和BJFS站的实测GPS高程时间序列数据进行验证。实验结果表明,该方法能够直接确定分界IMF的K值,降低计算量,在GPS高程时间序列降噪中较传统EMD方法更可靠。

A New Method for Noise Reduction Analysis of GPS Elevation Time Series Based on EMD

We propose a new EMD denoising method to solve the problem that the K value of the demarcated intrinsic mode function (IMF) cannot be determined directly in the process of Empirical mode decomposition (EMD) denoising when the number of IMF components of high frequency noise is less than the number of low frequency IMF components. This method uses an average period and energy density product index method to automatically determine the K value of the demarcated IMF, reconstructs the IMF components of high frequency noise, and subtracts the reconstructed noise from the original signal. The method is verified using the simulated data and the measured GPS elevation time series data of BJFS station. The experimental results show that the proposed method can directly determine the K value of the demarcated IMF and reduce the computational complexity. It was more reliable than the traditional EMD method in the noise reduction of GPS elevation time series.