GNSS jammer localization in urban areas based on prediction/optimization and ray-tracing

Jamming of Global Navigation Satellite System (GNSS) signals severely affects the security of critical infrastructures and applications. The localization of intentional jamming sources, jammers, is an important step in securing GNSS resilience as it provides the authorities with technical tools to prevent the jamming. However, jammers are difficult to localize in dense urban areas because the existence of multipath and non-line-of-sight propagation challenges conventional methods significantly. This challenge has not been comprehensively addressed in previous research. Motivated by this gap, a ray-tracing tool using 3-D city models is established to simulate jamming signal propagation with high precision and thereby augment the existing signal simulators, and measurements for localization are modeled by characterizing a commercial GNSS receiver under jamming conditions. Then, we propose a novel two-step strategy which consists of an ensemble subspace k-Nearest-Neighbor (KNN) as a raw-predictor and an improved gravitational searching algorithm (GSA) as a fine-optimizer. Based on this, two cloud-computing-based schemes using signal-matching and joint-localization in fine-optimizing stage are proposed. Finally, the proposed methods are evaluated in three typical urban areas, and their effectiveness and superiority over conventional least-squares method based on an empirical path-loss model are validated.