摘要
Atmospheric corrections are essential for Global Navigation Satellite System(GNSS)Precise Point Positioning RealTime Kinematic(PPP-RTK)service to achieve rapid convergence of precise positioning.Their quality directly infuences the accuracy and reliability of positioning results.However,several factors,including satellite elevation angle,difering levels of solar activity across regions,and spatial variation in a station network,can impact the accuracy of atmospheric corrections.Consequently,quality monitoring of atmospheric corrections remains a signifcant challenge.This paper introduces a quality monitoring method of atmospheric corrections using the leave-one-out cross-validation.In this method,a station is selected sequentially as the validation station to evaluate the atmospheric correction accuracy.The accuracy from each iteration is synthesized to derive the overall quality information of atmospheric corrections,which is then transmitted to the user to enhance positioning reliability and accuracy.The leave-one-out cross-validation method eliminates the need for supplementary monitoring stations and historical data,enabling the generation of atmospheric corrections to possess independent self-monitoring.Additionally,this method inherently adapts to varying grid scales and atmospheric conditions,obviating the need for specifc adaptations required by empirical models.The efectiveness of the proposed method is validated through PPP-RTK experiments using GNSS observations in the networks of various scales and atmospheric conditions.The results indicate that the quality information efectively refects the centimeter-level variations in atmospheric correction accuracy.Furthermore,applying the atmospheric correction quality information in PPP-RTK can improve positioning accuracy by more than 20%when abnormal atmospheric correction occurs.
基金
supported by the National Natural Science Foundation of China(No.42425401,42474023,42204017)
the Hubei Provincial Natural Science Foundation Exploration Program(2024040801020242)
the Fundamental Research Funds for the Central Universities(2042024kf0018)
the Major Program of Hubei Province(2023BAA02)
supported by the China Scholarship Council under Grant 202406270020.
