基于BP神经网络的GPT2w改进模型及全球精度分析

Improved GPT2 wmodel based on BP neural network and its global precision analysis

为提高全球卫星导航定位系统(global navigation satellite system,GNSS)的定位授时能力,方便用户在无法架设气象参数探测设备的条件下获得更高精度的对流层延迟估计,综述了当前主要的对流层延迟模型的发展,分析了两种空间分辨率的全球温压湿(global pressure and temperature 2wet,GPT2w)模型在全球大地测量观测系统(global geodetic observing system,GGOS)测站处的估计精度,根据GPT2w模型的对流层延迟估计误差与气象参数估计误差的关系,提出了将实测温度与模型经验拟合气象参数相结合的策略,建立了基于反向传播神经网络的GPT2w改进模型。仿真结果表明,改进模型在2017年全球GGOS测站处对流层延迟估计精度较GPT2w模型提升近32%,且对全球其他位置估计精度同样有改进效果,改进程度与GGOS测站疏密程度有关。

In order to improve the positioning and timing capacity of global navigation satellite system (GNSS), and make it convenient to obtain the tropospheric delay estimation with high accuracy without meteorological parameters detection equipment, this paper summarizes the development of the main tropospheric delay models, analyzes the estimation precision of the global pressure and temperature 2 wet (GPT2w) model in two different spatial resolutions at the global geodetic observing system (GGOS) stations, develops a strategy combining measured temperature and experience fitting meteorological parameters according to the relationship between estimation error of Zenith Tropospheric delay and that of meteorological parameters in GPT2w model, establishes a GPT2w improved model based on BP neural network. The simulation results show that the accuracy of Zenith Tropospheric delay estimation of the improved model at the global GGOS stations in 2017 is nearly 32% higher than that of the GPT2w model, and the accuracy of estimation of other global positions is also improved. The improvement is related to the density of GGOS stations.