GNSS snapshot techniques for quality of service monitoring

Research output: ThesisMaster's Thesis


The use of global navigation satellite systems and the associated permanent availability of position as well as precise time measurements become more and more a matter of course in many areas of everyday life. The information from GNSS satellites is used in many applications like civil engineering, energy industry, agriculture, civil protection, telecommunication, banking, transport, surveying and many others. Studies show that the main GNSS markets are road applications and location-based services (LBS). The number of GNSS users is constantly increasing and forecasts show that there will be one device per human in the next few years.
Due to the increasing number of applications and users, it becomes more important to consider not only the opportunities, but also the weaknesses and risks of a satellite-based position determination. Currently, many users are unaware of potential GNSS threats and their impacts. In recent years, GNSS applications have become the target of intentional interference attacks. Studies show that interference can cause both considerable economic and material damage, as interference signals can significantly influence the operation of GNSS receivers. In general, the impact of interference can lead to degraded position and timing accuracies or to a total failure of the positioning.
Successful mitigation techniques require a successful and reliable detection and classification of GNSS interference in advance. Classical approaches perform a continuous quality of service monitoring within the GNSS signal bands. Since the processing requirements and the amount of data to be processed are considered to be very high, a continuous monitoring is not suitable for all, especially low-cost, GNSS applications.
A GNSS positioning technique which uses only a limited amount of data (i.e. signal length) is called GNSS snapshot processing. This technique is used in GNSS receivers if only a limited amount of energy is available for computing the position solution, reducing the necessary computing power to a minimum. The receiver records only a few milliseconds of digitized GNSS signals and processes these signals in order to obtain a position, velocity and time (PVT) solution. Since no decoding of the navigation data takes place, the receiver needs to estimate the time of signal transmission on its own. This reduces the accuracy of the position solution down to several tens of meters. However, this accuracy is sufficient for tracking and tracing applications which have less stringent accuracy requirements.
Within this thesis GNSS snapshot techniques are investigated in more detail. Their potential regarding interference detection, using very short signal snapshots, is investigated.
The algorithms are implemented and tested with respect to their accuracy and precision with very short snapshot lengths and varying sampling frequencies. This is done to determine the minimum amount of required snapshot length and sampling frequency to successfully detect interference while maintaining a precise and accurate position solution. The time free Doppler and pseudorange positioning is investigated in more detail with simulated and recorded real-world data. Several detection methods have been implemented and those exploiting snapshot techniques are elaborated in more detail. The algorithms have been tested and analysed using simulated signals containing intentional interference such as different jamming events and spoofing attacks. These algorithms are then tested on real-world data without intentional interference to investigate their false alarm rates. The results are analysed and discussed and a conclusion is provided including an outlook on future improvements and possible further implementations.
Translated title of the contributionGNSS Qualitätsüberwachung mittels Momentaufnahmen digitaler Satellitensignale
Original languageEnglish
QualificationMaster of Science
Awarding Institution
  • Graz University of Technology (90000)
  • Hofmann-Wellenhof, Bernhard, Supervisor
  • Berglez, Philipp, Supervisor
Publication statusPublished - 7 Feb 2020


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