000 | 03068nam a22004695i 4500 | ||
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001 | 978-3-642-34836-5 | ||
003 | DE-He213 | ||
005 | 20140220082858.0 | ||
007 | cr nn 008mamaa | ||
008 | 130706s2013 gw | s |||| 0|eng d | ||
020 |
_a9783642348365 _9978-3-642-34836-5 |
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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. |
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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. |
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300 |
_aXXIII, 331 p. 129 illus., 127 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5053 |
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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 | 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 | ||
999 |
_c97559 _d97559 |