Characterization of multi-GNSS between-receiver differential code biases using zero and short baselines

Abstract Care should be taken to minimize adverse impact of receiver differential code biases (DCBs) on global navigation satellite system (GNSS)-derived ionospheric parameters. It is therefore of importance to ascertain the intrinsic characteristics of receiver DCBs, preferably in the context of new-generation GNSS. In this contribution, we present a method that enables time-wise retrieval of between-receiver DCBs (BR-DCBs) from dual-frequency, code-only measurements collected by a pair of co-located receivers. This method is applicable to the US GPS as well as to a new set of GNSS constellations including the Chinese BeiDou, the European Galileo and the Japanese QZSS. With the use of this method, we determine the multi-GNSS BR-DCB time-wise estimates covering a time period of up to 2 years (January 2013–March 2015) with a 30-s time resolution for five receiver-pairs (four zero and one short baselines). For the BR-DCB time-wise estimates pertaining to an arbitrary receiver-pair and constellation, we demonstrate their promising intraday stability by means of statistical hypothesis testing. We also find that the BeiDou BR-DCB daily weighted average (DWA) estimates show a dependence on satellite type, in particular for receiver-pairs of mixed types. Finally, we demonstrate that long-term variability in BR-DCB DWA estimates can be closely associated with hardware temperature variations inside the receivers.摘要利用全球导航卫星系统(global navigation satellite system, GNSS)研究电离层需要克服接收机差分码偏差(differential code bias, DCB)的不利影响。在新一代GNSS应用环境下, 准确地了解接收机DCB的相关特性尤为重要。本文报告了一种相对接收机DCB(between-receiver DCB, BR-DCB)的单历元估计方法, 其适用于美国GPS(global positioning system), 中国“北斗”、欧盟“伽利略”和日本“准天顶卫星系统”(quasi-zenith satellite system, QZSS)。通过处理5对接收机(含4组零基线和1组短基线)的多GNSS观测数据(采集于2013年1月至2015年3月, 30 s采样间隔), 本文获取并分析了相应的BR-DCB单历元估值。主要结论包括: BR-DCB单历元估值在一天内不存在显著变化; 由不同类 “北斗”卫星观测值所计算的BR-DCB估值可能会存在差异; BR-DCB估值的长期变化与接收机硬件温度高度相关。

[1]  Ningbo Wang,et al.  SHPTS: towards a new method for generating precise global ionospheric TEC map based on spherical harmonic and generalized trigonometric series functions , 2015, Journal of Geodesy.

[2]  Anthony J. Mannucci,et al.  New leveling and bias estimation algorithms for processing COSMIC/FORMOSAT‐3 data for slant total electron content measurements , 2011 .

[3]  Anthony J. Mannucci,et al.  A global mapping technique for GPS‐derived ionospheric total electron content measurements , 1998 .

[4]  Libo Liu,et al.  Solar activity effects of the ionosphere: A brief review , 2011 .

[5]  Tamar Frankel.  [The theory and the practice...]. , 2001, Tijdschrift voor diergeneeskunde.

[6]  Zishen Li,et al.  Two-step method for the determination of the differential code biases of COMPASS satellites , 2012, Journal of Geodesy.

[7]  Justine Spits,et al.  Total electron content monitoring using triple frequency GNSS data: A three-step approach , 2008 .

[8]  E. Sardón,et al.  Estimation of total electron content using GPS data: How stable are the differential satellite and receiver instrumental biases? , 1997 .

[9]  A. Q. Le,et al.  Impact of Galileo on Global Ionosphere Map Estimation , 2006, Journal of Navigation.

[10]  Wei Zhang,et al.  The variation of the estimated GPS instrumental bias and its possible connection with ionospheric variability , 2014 .

[11]  Isao Kawano,et al.  Japanese Experimental GPS Augmentation using Quasi-Zenith Satellite System (QZSS) , 2004 .

[12]  David A. Freedman,et al.  Statistical Models: Theory and Practice: References , 2005 .

[13]  Peter Steigenberger,et al.  Signal, orbit and attitude analysis of Japan’s first QZSS satellite Michibiki , 2011, GPS Solutions.

[14]  Lei Fan,et al.  Determination of the Differential Code Bias for Current BDS Satellites , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Qile Zhao,et al.  Near-field surface displacement and permanent deformation induced by the Alaska Mw 7.5 earthquake determined by high-rate real-time ambiguity-fixed PPP solutions , 2014 .

[16]  Xiaoqing Pi,et al.  JPL/USC GAIM: On the impact of using COSMIC and ground‐based GPS measurements to estimate ionospheric parameters , 2010 .

[17]  Yunbin Yuan,et al.  The ionospheric eclipse factor method (IEFM) and its application to determining the ionospheric delay for GPS , 2008 .

[18]  A. Garcia-Rigo,et al.  The IGS VTEC maps: a reliable source of ionospheric information since 1998 , 2009 .

[19]  Yidong Lou,et al.  Ionospheric effects in uncalibrated phase delay estimation and ambiguity-fixed PPP based on raw observable model , 2015, Journal of Geodesy.

[20]  Zuo Xiao,et al.  Accuracy analysis of the GPS instrumental bias estimated from observations in middle and low latitudes , 2010 .

[21]  Yang Gao,et al.  Ionospheric modeling using GPS data , 2005 .

[22]  Yuanxi Yang,et al.  Contribution of the Compass satellite navigation system to global PNT users , 2011 .

[23]  Weiming Tang,et al.  Performance Analysis of Ionosphere Monitoring with BeiDou CORS Observational Data , 2014, Journal of Navigation.

[24]  Manuel Hernández-Pajares,et al.  The ionosphere: effects, GPS modeling and the benefits for space geodetic techniques , 2011 .

[25]  Peter J. G. Teunissen,et al.  BeiDou Inter-Satellite-Type Bias Evaluation and Calibration for Mixed Receiver Attitude Determination , 2013, Sensors.

[26]  Baocheng Zhang,et al.  Extraction of line-of-sight ionospheric observables from GPS data using precise point positioning , 2012, Science China Earth Sciences.

[27]  A. Rius,et al.  Estimation of the transmitter and receiver differential biases and the ionospheric total electron content from Global Positioning System observations , 1994 .

[28]  Yunbin Yuan,et al.  Differential areas for differential stations (DADS): A new method of establishing grid ionospheric model , 2002 .

[29]  Xinan Yue,et al.  Is the long-term variation of the estimated GPS differential code biases associated with ionospheric variability? , 2016, GPS Solutions.

[30]  Zhang Baocheng,et al.  Extraction of line-of-sight ionospheric observables from GPS data using precise point positioning , 2012 .

[31]  Wei Zhang,et al.  The influence of geomagnetic storms on the estimation of GPS instrumental biases , 2009 .

[32]  Sandro M. Radicella,et al.  Calibration errors on experimental slant total electron content (TEC) determined with GPS , 2007 .

[33]  C. Shi,et al.  Precise orbit determination of BeiDou constellation based on BETS and MGEX network , 2014, Scientific Reports.

[34]  Peter Steigenberger,et al.  Differential Code Bias Estimation using Multi‐GNSS Observations and Global Ionosphere Maps , 2014 .

[35]  O. Montenbruck,et al.  Precise orbit determination of GIOVE-B based on the CONGO network , 2011 .

[36]  Allan T. Weatherwax,et al.  Accuracy of GPS total electron content: GPS receiver bias temperature dependence , 2013 .

[37]  Claudio Brunini,et al.  Accuracy assessment of the GPS-based slant total electron content , 2009 .