Data Processing and Adjustment

  • Joseph L. Awange
Part of the Environmental Science and Engineering book series (ESE)


In this chapter, the necessary data processing or post-processing following field measurements is presented. First, the general procedures undertaken to process baseline data are considered, followed by the adjustment of network observations.


Ambiguity Resolution International GNSS Service Army Corps Integer Ambiguity Cycle Slip 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. AUSPOS (2006) Australian online GPS processing service. Accessed on 14 May 2009
  2. Awange JL, Grafarend EW (2002) Algebraic solution of GPS pseudo-ranging equations. J GPS Solut 4:20–32CrossRefGoogle Scholar
  3. Awange JL, Grafarend EW (2005) Solving algebraic computational problems in geodesy and geoinformatics, 5th edn. Springer, BerlinGoogle Scholar
  4. Awange JL, Grafarend EW, Palánczz B, Zaletnyik P (2010) Algebraic geodesy and geoinformatics. 2nd edn. Springer, BerlinCrossRefGoogle Scholar
  5. Craymer MR, Beck N (1992) Session versus single-baseline GPS processing. In: Proceedings of 5th international technical meeting of the satellite division of the US Institute of Navigation, 16–18 Sept 1992, pp 995-1004Google Scholar
  6. Dawson J, Govind R, Manning J (2010) The AUSLIG Online GPS Processing System (AUSPOS). or Accessed on 8 Oct 2010
  7. Grafarend EW, Awange JL (2012) Linear and nonlinear models—fixed effects, random effects, and total least squares. Springer, BerlinGoogle Scholar
  8. Han S, Rizos C (1995) Selection and scaling of simultaneous baselines for GPS network adjustment, or correct procedures for processing trivial baselines. Geomat Res Australas 63:51–66Google Scholar
  9. Heck B (1987) Rechenverfahren und Auswertemodelle der Landesvermessung. Wichmann Verlag, KarlsruheGoogle Scholar
  10. Hofman-Wellenhof B, Lichtenegger H, Collins J (2001) Global positioning system: theory and practice. 5th edn. Springer, WienCrossRefGoogle Scholar
  11. Hofman-Wellenhof B, Lichtenegger H, Wasle E (2008) GNSS global navigation satellite system: GPS, GLONASS; Galileo and more. Springer, WienGoogle Scholar
  12. Koch KR (1999) Parameter estimation and hypothesis testing in linear models, 2nd edn. Springer, HeidelbergGoogle Scholar
  13. Leick A (2004) GPS satellite surveying. 3rd edn. Wiley, New YorkGoogle Scholar
  14. Luo X (2012) Extending the GPS stochastic model by means of signal quality measures and ARMA processes. Doctoral thesis at the Geodetic Institute, Faculty of Civil Engineering, Geo and Environmental Sciences, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyGoogle Scholar
  15. National Geodetic Survey (2006) Guidelines for new and existing continuously operating reference stations (CORS). NOAA, Silver SpringGoogle Scholar
  16. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in Fortran 77: the art of scientific computing. 2nd edn. Cambridge University Press, CambridgeGoogle Scholar
  17. Teunissen PJG, Jonkman NF, Tiberius CCJM (1998) Weighting GPS dual frequency observations: bearing the cross of cross correlation. GPS Soult 2(2):28–37. doi: 10:1007/PL00000033 Google Scholar
  18. Teunissen PJG, de Jonge PJ, Tiberius CCJM (1995) The LAMBDA method for fast GPS surveying.In: Proceedings of international symposium GPS technology applications, Bucharest, Romania, 26-29 Sept 1995, pp 203–210Google Scholar
  19. US Army Corps of Engineers (2007) NAVSTAR Global Positioning System surveying. Engineering and design manual, EM 1110-1-1003Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Joseph L. Awange
    • 1
    • 2
    • 3
    • 4
  1. 1.Maseno UniversityMasenoKenya
  2. 2.Curtin UniversityPerthAustralia
  3. 3.Karlsruhe Institute of TechnologyKarlsruheGermany
  4. 4.Kyoto UniversityKyotoJapan

Personalised recommendations