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Author Luo, Xiaoguang

Title GPS Stochastic Modelling : Signal Quality Measures and ARMA Processes
Published Dordrecht : Springer, 2013
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Description 1 online resource (345 pages)
Series Springer Theses
Springer theses.
Contents Supervisor's Foreword; Acknowledgments; Contents; Acronyms; 1 Introduction; 1.1 Problem Statement; 1.2 State of the Art; 1.3 Objectives of this Thesis; 1.4 Outline of the Thesis; References; 2 Mathematical Background; 2.1 Parameter Estimation in Linear Models; 2.1.1 Estimators and Optimisation Criteria; 2.1.2 Weighted Least-Squares Estimation; 2.1.3 Best Linear Unbiased Estimation; 2.2 Time Series Analysis; 2.2.1 Classical Decomposition Model; 2.2.2 (Partial) Autocorrelation Function; 2.2.3 Autoregressive Moving Average Processes; 2.2.4 An Example of the Classical Decomposition Model
2.3 Statistical Hypothesis Tests2.3.1 Hypothesis Testing; 2.3.2 Tests for Normality; 2.3.3 Tests for Trend; 2.3.4 Tests for Stationarity; 2.3.5 Tests for Uncorrelatedness; 2.4 Wavelet Transforms; 2.4.1 Wavelets and Morlet Wavelet; 2.4.2 Continuous Wavelet Transform; 2.4.3 Discrete Wavelet Transform; 2.4.4 An Example of Wavelet Transforms; References; 3 Mathematical Models for GPS Positioning; 3.1 Global Positioning System; 3.1.1 Reference and Time Systems; 3.1.2 GPS Segments; 3.1.3 GPS Signals; 3.1.4 GPS Observations; 3.1.5 Linear Combinations; 3.2 Precise Point Positioning
3.2.1 Introduction3.2.2 Functional Model; 3.2.3 Error Sources and Effects; 3.2.4 Stochastic Model; 3.3 Relative Positioning; 3.3.1 Introduction; 3.3.2 Functional Model; 3.3.3 Error Sources and Effects; 3.3.4 Stochastic Model; References; 4 Data and GPS Processing Strategies; 4.1 Selecting Sites and Forming Baselines; 4.2 Relative Positioning Processing Strategies; 4.2.1 Processing Steps; 4.2.2 A Long-Term Case Study; 4.2.3 A Short-Term Case Study; 4.3 PPP Processing Strategies; 4.3.1 Processing Steps; 4.3.2 A Long-Term Case Study; References
5 Observation Weighting Using Signal Quality Measures5.1 Signal-to-Noise Ratio; 5.2 Review of Previous Work; 5.3 SNR-Based Weighting Model; 5.3.1 Model Realisation; 5.3.2 Model Comparison; 5.3.3 Model Implementation; 5.4 Concluding Remarks; References; 6 Results of SNR-Based Observation Weighting; 6.1 Case Study 1: Long-Term Relative Positioning; 6.1.1 SNR Extremes and Observation Weights; 6.1.2 Effects on Ambiguity Resolution; 6.1.3 Effects on Troposphere Parameters; 6.1.4 Effects on Coordinate Estimates; 6.2 Case Study 2: Short-Term Relative Positioning
6.2.1 SNR Extremes and Observation Weights6.2.2 Effects on Ambiguity Resolution; 6.2.3 Effects on Troposphere Parameters; 6.2.4 Effects on Coordinate Estimates; 6.3 Concluding Remarks; References; 7 Residual-Based Temporal Correlation Modelling; 7.1 Review of Previous Work; 7.2 Residual Decomposition; 7.2.1 Studentised Residuals; 7.2.2 Decomposition Model; 7.2.3 Vondrák Filtering; 7.2.4 Outlier Handling; 7.2.5 Sidereal Stacking; 7.3 ARMA Modelling; 7.3.1 AR Estimation; 7.3.2 MA Estimation; 7.3.3 ARMA Estimation; 7.3.4 AR-MA Identification; 7.4 Concluding Remarks; References
Summary Global 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 dat
Notes 8 Results of Residual-Based Temporal Correlation Modelling
Bibliography Includes bibliographical references
Notes Print version record
Subject Geospatial data -- Mathematical models.
Stochastic models.
Digital communications.
Engineering.
Form Electronic book
ISBN 3642348351
364234836X
9783642348358
9783642348365