000 03156nam a22004937a 4500
001 sulb-eb0024405
003 BD-SySUS
005 20160413122438.0
007 cr nn 008mamaa
008 130706s2013 gw | s |||| 0|eng d
020 _a9783642348365
_9978-3-642-34836-5
024 7 _a10.1007/978-3-642-34836-5
_2doi
050 4 _aGA102.4.R44
050 4 _aG70.39-70.6
072 7 _aRGW
_2bicssc
072 7 _aTEC036000
_2bisacsh
082 0 4 _a910.285
_223
100 1 _aLuo, Xiaoguang.
_eauthor.
245 1 0 _aGPS Stochastic Modelling
_h[electronic resource] :
_bSignal Quality Measures and ARMA Processes /
_cby Xiaoguang Luo.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXXIII, 331 p. 129 illus., 127 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
505 0 _aIntroduction -- Mathematical Background -- Mathematical Models for GPS Positioning -- Data and GPS Processing Strategies -- Observation Weighting Using Signal Quality Measures -- Results of SNR-based Observation Weighting -- Residual-based Temporal Correlation Modelling -- Results of Residual-based Temporal Correlation Modelling -- Conclusions and Recommendations -- Quantiles of Test Statistics -- Derivations of Equations -- Additional Graphs -- Additional Tables.
520 _aGlobal Navigation Satellite Systems (GNSS), such as GPS, have become an efficient, reliable and standard tool for a wide range of applications. However, when processing GNSS data, the stochastic model characterising the precision of observations and the correlations between them is usually simplified and incomplete, leading to overly optimistic accuracy estimates. This work extends the stochastic model using signal-to-noise ratio (SNR) measurements and time series analysis of observation residuals. The proposed SNR-based observation weighting model significantly improves the results of GPS data analysis, while the temporal correlation of GPS observation noise can be efficiently described by means of autoregressive moving average (ARMA) processes. Furthermore, this work includes an up-to-date overview of the GNSS error effects and a comprehensive description of various mathematical methods.
650 0 _aGeography.
650 0 _aRemote sensing.
650 0 _aMathematical physics.
650 1 4 _aGeography.
650 2 4 _aRemote Sensing/Photogrammetry.
650 2 4 _aMathematical Applications in the Physical Sciences.
650 2 4 _aSignal, Image and Speech Processing.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642348358
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-34836-5
912 _aZDB-2-EES
942 _2Dewey Decimal Classification
_ceBooks
999 _c46497
_d46497