电子测量与仪器学报

孔德龙,刘春,何敏,汪志宁.GNSS 多系统 PPP 中强跟踪自适应Kalman 滤波的应用[J].电子测量与仪器学报,2023,37(10):24-31

GNSS 多系统 PPP 中强跟踪自适应Kalman 滤波的应用

Application of strong tracking adaptive Kalman filter in GNSS multi-system PPP

DOI：

英文关键词:PPP Kalman filtering adaptive estimation fading factor measurement noise

基金项目:合肥市北斗卫星导航重大应用示范项目资助

作者	单位
孔德龙	1. 合肥工业大学电气与自动化工程学院
刘春	1. 合肥工业大学电气与自动化工程学院
何敏	1. 合肥工业大学电气与自动化工程学院
汪志宁	2. 国网黄山供电公司

Author	Institution
Kong Delong	1. School of Electrical and Automation Engineering, Hefei University of Technology
Liu Chun	1. School of Electrical and Automation Engineering, Hefei University of Technology
He Min	1. School of Electrical and Automation Engineering, Hefei University of Technology
Wang Zhining	2. State Grid Huangshan Power Supply Company

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中文摘要:

精密单点定位(precise point positioning, PPP)技术由于操作简单、定位精度高,现已广泛应用于许多领域。针对 PPP 解算过程中周围环境改变可能带来的观测噪声和多路径效应,传统滤波算法无法解决其导致的精度下降的问题,本文提出一种强跟踪自适应 Kalman 滤波(strong tracking adaptative Kalman filtering, SAKF)算法,通过引入渐消因子调整预测误差值,同时使用 IGGⅢ函数方法重构测量噪声协方差,从而实现 PPP 解算。实验结果表明,在静态解算时,SAKF 定位精度较传统算法提升约 20%,在仿动态解算时,SAKF 定位精度提升约 55% ~ 60%,同时具有更好的收敛稳定性。

英文摘要:

Precise point positioning (PPP) technology has been widely used in many fields because of its simple operation and high positioning accuracy. Aiming at the observation noise and multipath effect that may be caused by the change of surrounding environment, the traditional filtering algorithm cannot solve the problem of precision decline caused by it, this paper proposes a strong tracking adaptive Kalman filtering (SAKF) algorithm. The fading factor is introduced to adjust the prediction error value, and the measurement noise covariance is reconstructed by IGGⅢ function method, to achieve realize PPP solution. The experimental results show that the positioning accuracy of SAKF is improved by about 20% compared with the traditional algorithm in static solution, and it is improved by about 55% ~ 60% in quasi-dynamic solution, and it has better convergence stability.

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