姜卫平, 王锴华, 李昭, 周晓慧, 马一方, 马俊. GNSS坐标时间序列分析理论与方法及展望[J]. 武汉大学学报 ( 信息科学版), 2018, 43(12): 2112-2123. DOI: 10.13203/j.whugis20180333
引用本文: 姜卫平, 王锴华, 李昭, 周晓慧, 马一方, 马俊. GNSS坐标时间序列分析理论与方法及展望[J]. 武汉大学学报 ( 信息科学版), 2018, 43(12): 2112-2123. DOI: 10.13203/j.whugis20180333
JIANG Weiping, WANG Kaihua, LI Zhao, ZHOU Xiaohui, MA Yifang, MA Jun. Prospect and Theory of GNSS Coordinate Time Series Analysis[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2112-2123. DOI: 10.13203/j.whugis20180333
Citation: JIANG Weiping, WANG Kaihua, LI Zhao, ZHOU Xiaohui, MA Yifang, MA Jun. Prospect and Theory of GNSS Coordinate Time Series Analysis[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2112-2123. DOI: 10.13203/j.whugis20180333

GNSS坐标时间序列分析理论与方法及展望

Prospect and Theory of GNSS Coordinate Time Series Analysis

  • 摘要: 长期累积的全球卫星导航系统(Global Navigation Satellite System,GNSS)基准站坐标时间序列为大地测量学及地球动力学研究提供了基础数据。通过完善GNSS数据处理模型及策略,研究造成非线性运动的机制并进行有效建模,可以获得测站准确的位置和速度,不仅有助于合理解释板块构造运动,建立和维持动态地球参考框架,而且能更好地研究冰后回弹及海平面变化,反演冰雪质量变迁等地球动力学过程。首先从基准站坐标的精确获取、时间序列模型构建、时间序列信号分析等方面描述了GNSS坐标时间序列分析的理论与处理方法;其次,探讨了坐标时间序列噪声模型构建技术,给出了严密三维噪声模型构建方法;然后,疏理了坐标时间序列中非线性变化成因机制的研究进展;最后,总结了基于GNSS坐标时间序列的应用领域,并展望了其未来的发展方向。

     

    Abstract: The long-term accumulated coordinate time series of Global Navigation Satellite System (GNSS) reference stations provide basic data for geodesy and geodynamic studies. Through improving precise GNSS data processing models and strategies, together with investigating physical mechanism and properly modeling of the non-linear variations, the precise position and velocity of GNSS stations can be estimated. This will not only help interpret the plate tectonics reasonably, establish and maintain the terrestrial reference frame, but also contribute to investigating the geodynamic processes, such as post-glacial rebound, sea level change, as well as snow and ice redistribution inversion, et al. In this paper, the basic theory and methodology of GNSS coordinate time series analysis is reviewed by introducing the model estimation of station coordinates time series and the signal analysis. Then, noise modeling technique for GNSS coordinate time series is outlined. Particularly, a rigorous 3-D noise modeling method is proposed to best describe the stochastic process. After that, the advances in investigating physical mechanism of the non-linear variations in GNSS coordinate time series are summarized. Finally, we outline the scientific and engineering applications of GNSS coordinate time series, and discuss the prospect of future research.

     

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