卫星伪距多路径时空建模研究

目前,多路径误差是制约GPS技术向更高精度发展的主要误差源。为了减小静态观测环境下的多路径误差,基于多路径误差周期重复性的特点,提出了一种自适应噪声分析结合经验模态分解的方法处理原始数据,采用多项式拟合构建了函数模型,并利用该函数模型对相邻天的观测数据进行处理。实验结果表明,利用所提方法对观测数据进行处理,可有效减小多路径效应误差,并且可以在一定程度上改正坐标序列中包含的多路径误差,多路径误差的削弱程度可达35%以上,有效提升了GPS定位数据的准确度。

Spatiotemporal Modeling of Satellite Pseudo-Distance Multipath

At present, multipath error is the main error source restricting the development of GPS technology to higher precision. In order to reduce the multi-path error in the static observation environment, an adaptive noise analysis combined with empirical mode decomposition method is proposed to process the original data based on the characteristics of periodic repeatability of multipath error, and the function model is constructed by polynomial fitting. The function model is used to process the observation data of adjacent days. The experimental results show that the multipath effect error can be effectively reduced by using the method to process the observed data, and the multi-path error contained in the coordinate sequence can be corrected to a certain extent. The multipath error can be reduced by more than 35%, effectively improving the accuracy of GPS positioning data.