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Session F3b: GNSS Robustness to Vulnerabilities 1

Simultaneous Classification and Searching Method for Jammer Localization in Urban Areas Using KNN-GSA and Ray-Tracing
Zhe Yan, Outi Savolainen, Xinhua Tang, Laura Ruotsalainen, Department of Computer Science, University of Helsinki
Date/Time: Thursday, Sep. 19, 11:26 a.m.

Global Navigation Satellite Systems (GNSS) are the primary providers of precise Position, Navigation, and Timing (PNT) information for critical infrastructure. Consequently, actively locating intentional interference, jamming, sources is essential for ensuring GNSS resilience enabling authorities to prevent ongoing harmful activities. However, conventional jammer localization methods face significant limitations in urban environments, particularly in addressing non-line-of-sight and multipath jamming receptions. In our previous work, we proposed a ray-tracing method using 3D city models to simulate jamming propagation in real urban settings and developed a two-step method for jammer localization. This paper extends our previous work by investigating the varying impacts of jamming signals on different GNSS signal models and addressing the shortcomings in the predictions of a classifier we implemented as the basis of our method. We conduct extensive testing and comparative analysis to evaluate the performance of our proposed methods across different GNSS signal models. Additionally, we expand our analysis to assess the method’s effectiveness when the stand-alone classifier makes incorrect predictions.

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