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GPS Stochastic Modelling

Signal Quality Measures and ARMA Processes

  • Xiaoguang Luo

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Xiaoguang Luo
    Pages 1-6
  3. Xiaoguang Luo
    Pages 7-53
  4. Xiaoguang Luo
    Pages 55-116
  5. Xiaoguang Luo
    Pages 117-136
  6. Xiaoguang Luo
    Pages 163-191
  7. Xiaoguang Luo
    Pages 193-225
  8. Xiaoguang Luo
    Pages 289-293
  9. Back Matter
    Pages 295-331

About this book

Introduction

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 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.

Keywords

ARMA Process AutoRegressive Moving Average Process Hypothesis Testing Signal-to-Noise Ratio (SNR) Stochastic Model of GNSS Observations Wavelet Analysis

Authors and affiliations

  • Xiaoguang Luo
    • 1
  1. 1., Geodetic InstituteKarlsruhe Institute of Technology (KIT)KarlsruheGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-34836-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Earth and Environmental Science
  • Print ISBN 978-3-642-34835-8
  • Online ISBN 978-3-642-34836-5
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • Buy this book on publisher's site
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