Refining ionospheric delay modeling for undifferenced and uncombined GNSS data processing
作者: Zhao, QL (Zhao, Qile); Wang, YT (Wang, YinTong); Gu, SF (Gu, Shengfeng); Zheng, F (Zheng, Fu); Shi, C (Shi, Chuang); Ge, MR (Ge, Maorong); Schuh, H (Schuh, Harald)
来源出版物: JOURNAL OF GEODESY 卷: 93 期: 4 页: 545-560 DOI: 10.1007/s00190-018-1180-9 出版年: APR 2019
摘要: To access the full capabilities of multi-frequency signals from the modernized GPS, GLONASS and newly deployed BDS, Galileo, the undifferenced and uncombined observable model in which the individual signal of each frequency is treated as independent observable has drawn increasing interest in GNSS community. The ionosphere delay is the major issue in the undifferenced and uncombined observable model. Though several ionosphere delay parameterization approaches have been promoted, we argue that the functional model with only deterministic characteristic may not follow the irregular spatial and temporal variations. On the contrary, when the ionosphere delay is estimated as random walk or even white noise with only stochastic characteristic, the ionosphere terms turn out to be non-estimable or not sensitive to their absolute value. In the authors' previous study, we have developed the deterministic plus stochastic ionosphere model, denoted as DESIGN, in which the deterministic part expressed with second-order polynomial is estimated as piece-wise constant over 5min and the stochastic part is estimated as random walk with constrains derived based on statistics of 4weeks data in 2010. In this contribution, we further model the deterministic part with Fourier series and update the variogram of the stochastic part accordingly based on two-year data collected by about 150 stations. From the statistic studies, it is concluded that the main frequency components are identical for different coefficients, different stations, as well as different ionosphere activity status, but with varying amplitude. Thus, in the Fourier series expression of the deterministic part, we fix the frequency and estimate the amplitude as daily constant unknowns.