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The variation of the estimated GPS instrumental bias and its possible connection with ionospheric variability

Science China(Technological Sciences) 2014年第1期57卷 67-79页

作者： ZHANG DongHe SHI Hao JIN YaQi ZHANG Wei HAO YongQiang XIAO Zuo Department of Geophysics Peking University Department of Geodesy and Geomatics Engineering University of New Brunswick Fredericton 4400 New Brunswick Canada

Based on the analysis of the satellite DCB data estimated by our method and the Center for Orbit Determination in Europe(CODE)from 1999 to 2011,the features of the temporal variation of differential code biases(DCB)are ***,there are three types of variations in DCB on different time *** first one is the day-to-day variation that exhibits more obviously in solar maximum *** second one is the variation with about one year periodic variation that behaves more obviously from 1999 to *** last one is the monotonously descending tendency from 1999 to *** the basic ionospheric approximation in DCB estimation method,the features of the variability of the ionospheric morphology from 1999to 2010 are also displayed based on the ionospheric characteristic *** can be concluded that the day-to-day and annual variation of the estimated global positioning system(GPS).DCB is related to the ionospheric *** variation of DCBs on solar cycle time scale includes the real hardware DCBs and pseudo-DCBs induced by ionospheric *** doubt,these kinds of"pseudo"variations of DCB will affect the precision of ionospheric total electron content(TEC)derived from the GPS *** addition,this study is helpful for evaluating the influence of ionospheric weather on TEC derivation and is also useful for developing one estimation method of DCB with more stability and precision through introducing a more practical ionospheric model.

关键词： DCB ionospheric variability GPS instrumental bias solar cycle

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A neural network based ionospheric TEC derivation method with only carrier phase observations

A neural network based ionospheric TEC derivation method wit...

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第十一届中国卫星导航年会

作者： Weijun Lu Guanyi Ma National Astronomical Observatories Chinese Academy of Sciences University of Chinese Academy of Sciences

In derivation of ionospheric total electron content(TEC) with global navigation satellite system(GNSS), because the pseudorange contains large observation noise and the carrier phase includes unknown

关键词： Ionosphere Total electron content Artificial neural network instrumental bias Root mean square error

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