GNSS pseudorange-based analysis under clear and spoofed scenarios
Verbaityte, Paulina (2024)
Verbaityte, Paulina
2024
Sähkötekniikan DI-ohjelma - Master's Programme in Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
Hyväksymispäivämäärä
2024-12-30
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-2024122011508
https://urn.fi/URN:NBN:fi:tuni-2024122011508
Tiivistelmä
Global Navigation Satellite Systems (GNSS) are critical for accurate positioning, navigation, and timing solutions in applications such as aviation, transportation and smart grids. However, due to the known signal structure and the fact that the received signal is low power, GNSS signals are vulnerable not only to unintentional interference such as multipath but also to intentional attacks, which can result in no or incorrect positioning solutions. This is very dangerous and can lead to harmful outcomes.
This thesis deals with GNSS spoofng threats and suggests some solutions by per forming pseudorange-level analysis on both Android raw GNSS data and high quality in-lab measurements. The results showed that monitoring the distance be tween position solutions or comparing recalculated pseudoranges with the initial measurements could correctly indicate if spoofng is happening. Moreover, in-lab data analysis, such as TAU and FGI datasets, showed some signifcant diferences between the numbers of level crossing rates of double diferential pseudoranges when comparing spoofng data and real signals. However, more advanced spoofng attacks did not show same results.
The study shows that as spoofers become more sophisticated, traditional statistical analysis might not be enough. Thus, the need for more advanced algorithms with ML is evident. Moreover, to improve reliability, GNSS should be used with together with other systems such as cellular or LEO technologies.
This thesis deals with GNSS spoofng threats and suggests some solutions by per forming pseudorange-level analysis on both Android raw GNSS data and high quality in-lab measurements. The results showed that monitoring the distance be tween position solutions or comparing recalculated pseudoranges with the initial measurements could correctly indicate if spoofng is happening. Moreover, in-lab data analysis, such as TAU and FGI datasets, showed some signifcant diferences between the numbers of level crossing rates of double diferential pseudoranges when comparing spoofng data and real signals. However, more advanced spoofng attacks did not show same results.
The study shows that as spoofers become more sophisticated, traditional statistical analysis might not be enough. Thus, the need for more advanced algorithms with ML is evident. Moreover, to improve reliability, GNSS should be used with together with other systems such as cellular or LEO technologies.