GNSS DATA PROCESSING – Volume I : Fundamentals and Algorithms

欧州宇宙機関(ESA)
[ Link to esa.int ]

Overview – This book is inspired by the welcome package that we offer to our doctoral students when they start their activities in the research group. The original package has been updated and compiled into two volumes which contain a self-learning course and software tools aimed at providing the necessary background to start work in an operative way in Global Navigation Satellite System (GNSS). The design and contents are focused on the instrumental use of the concepts and techniques involved in GNSS navigation and it is intended to include all the elements needed to understand how the system works and how to work with it. In this way, after working through the two volumes, the students should be able to develop their own tools for high-accuracy navigation, implementing the algorithms and expanding the skills learned.
　　The first volume is devoted to the theory, providing a summary of the GNSSs (GPS, Glonass, Galileo and Beidou) fundamentals and algorithms. The second volume is devoted to laboratory exercises, with a wide range of selected practical examples going further into the theoretical concepts and their practical implementation. The exercises have been developed with a specialised software package provided on a CD-ROM together with a set of selected data files for the laboratory sessions. This is an end-to-end GNSS course addressed to all those professionals and students who wish to undertake a deeper study of satellite navigation, targeting the GNSS data processing and analysis issues.
　　Starting from a review of the GNSS architecture, the contents of Volume I range from the analysis of basic observables (code pseudorange and carrier phase measurements) to setting up and solving the navigation equations for Standard Point Positioning (SPP) and Precise Point Positioning (PPP). It involves, in particular, an accurate modelling of GNSS measurements (up to the centimetre level of accuracy or better) as well as the required mathematical background to achieve the high-accuracy positioning goal. Parameter estimation techniques such as least squares and Kalman filtering are explained from a conceptual point of view, looking towards implementation at an algorithmic level.