基于Kalman滤波的大规模GNSS网参数估计方法

针对传统方法存在的缺陷,研究了利用Kalman滤波技术进行大规模GNSS网参数(主要包括测站位置参数、卫星轨道参数及极移参数)估计的理论方法与关键技术,并利用40个全球均匀分布的IGS站多天的观测数据对理论成果进行了验证。结果表明,本文估计得到的测站位置参数与IGS结果各分量较差的RMS值分别为0.85、1.1、1.21 cm,得到的卫星轨道参数外推1 h后与IGS最终星历各分量较差的RMS值分别为9.8、8.6、7.2 cm,得到的极移参数与IERS结果的较差基本在1 mas之内;该方法具有较高的估值精度,可有效地用于GNSS网各类参数的估计。

Research and Case Analysis of Estimation Method Based on Kalman Filtering for Geodetic Parameters from GNSS Networks

Focusing on the shortcomings of traditional methods,theory and key technologies with using Kalman filtering were researched for estimating geodetic parameters from GNSS network,such as station coordinates,satellite orbits and polar motion parameters.Then the achievements were verified with days of observations from 40 globally distributed IGS stations.The results indicated that the RMS values of the difference between IGS station coordinates estimated here and that advised by IGS were 0.85,1.1 and 1.21 cm in X,Y,Z direction respectively.The RMS values achieved in the similar way for the IGS final orbits and that acquired by extrapolating for an hour with the orbit parameters estimated here were 9.8,8.6 and 7.2 cm.The difference between the estimated polar motion parameters and those advised by IERS were mostly within 1 mas.The results showed that the algorithm used here was precise and could be utilized to estimate the geodetic parameters from GNSS networks.