基于BP神经网络的昆明天顶湿延迟模型  

Kunming zenith wet delay model based on a backpropagation neural network

作　　者：丁仁军 王友昆 张君华 刘晨 DING Renjun;WANG Youkun;ZHANG Junhua;LIU Chen(Kunming Surveying and Mapping Institute,Kunming 650051,China;School of Geodesy and Geomatics,Wuhan University,Wuhan 430079,China;Guangxi Key Laboratory of Spatial Information and Geomatics,Guilin University of Technology,Guilin 541004,China)

机构地区：[1]昆明市测绘研究院,云南昆明650051 [2]武汉大学测绘学院,湖北武汉430079 [3]桂林理工大学广西空间信息与测绘重点实验室,广西桂林541004

出　　处：《测绘通报》2022年第3期107-110,共4页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(41721003,41874033);昆明市卫星定位综合服务系统整合技术服务项目(JS2020-03);昆明市卫星定位综合服务系统扩展升级专项资金(JS2021-02);昆明市卫星定位综合服务系统整合建设及关键技术研究(昆测研202003);广西空间信息与测绘重点实验室资助课题(19-185-10-17)。

摘　　要：为了满足昆明市卫星定位综合服务系统(KMCORS)对高精度天顶湿延迟(ZWD)的需要,本文开发了适用于昆明地区的ZWD模型KM。KM模型是根据昆明探空站2015—2018年的探空资料,基于误差反向传播(BP)神经网络建立的,同时采用2019年的探空数据,验证了KM模型的预测性能。测试结果表明,与广泛使用的SA模型相比,KM模型的RMSE由4.0 cm降至2.2 cm,精度提升了45%;KM和SA模型的Bias分别为0和-3.1 cm。该结果表明KM模型对ZWD估计具有无偏性,而SA模型在高原区存在过度估计的问题,KM模型具有比SA经验模型更优的预测性能,其应用将有助于提升KMCORS的服务质量。For the high-precision zenith wet delay(ZWD)used in Kunming continuously operating reference stations(KMCORS),this paper developes the Kunming model(KM)suitable for the KM area.According to the sounding data of the KM sounding station from 2015 to 2018,the KM model is generated based on a backpropagation(BP)neural network.This study then validates the prediction performance of the KM model using the sounding data during 2019.Test results show that the RMSE of the KM model decreases from 4.0 cm to 2.2 cm compared with the widely used Saastamoninen(SA)model,indicating its 45%accuracy improvement.Additionally,the Bias of the KM and SA models are 0 and-3.1 cm,respectively,suggesting that the ZWD estimation of the KM model is unbiased,while the SA model has the problem of overestimation in the plateau area.In summary,the KM model has better prediction performance than the SA empirical model,and the application of the KM model will help to improve the service quality of KMCORS.

关 键 词：昆明CORS 天顶湿延迟 BP神经网络 Saastamoninen模型 

分 类 号：P228.[天文地球—大地测量学与测量工程]