GNSS Interference Detection in Maritime Navigation

Kurzfassung

Global Navigation Satellite Systems (GNSS) have become the cornerstone of Positioning, Navigation, and Timing (PNT) services, playing a pivotal role in modern maritime navigation. Beyond providing absolute and accurate positioning, GNSS serves as the primary timing reference for a large number of systems onboard a vessel, such as the Electronic Chart Display and Information Systems (ECDIS) or the Automatic Identification Systems (AIS). However, GNSS is extremely vulnerable to radio frequency interference, such as jamming and spoofing, which poses a critical threat to the integrity of positioning solutions, especially in safety-critical maritime environments. Therefore, any degradation in GNSS signals can compromise the operation of such systems which strongly depend on the provision of accurate PNT information, which consequently can jeopardize the vessel’s safety. A major challenge in detecting interference lies in the accessibility to the complete GNSS receiver chain, including the antenna, front-end and baseband blocks. While advanced digital signal processing (DSP) techniques offer high sensitivity to interference, they are typically inaccessible to users of mass-market GNSS receivers due to their high computational complexity, hardware requirements, and cost. To address this, we propose a more pragmatic approach: interference detection at the observation level using Machine Learning (ML) techniques. We use the GNSS observables (i.e., pseudorange and carrier-phase measurements) extracted from the receiver output, which are then processed using ML models trained on real-world jamming conditions. This method is particularly suitable for real-world deployment, as it operates on standard receiver outputs (i.e., RINEX) and does not require access to internal signal processing stages or highly demanding DSP techniques. ML is particularly well-suited for this jamming detection because it can identify complex, non-linear patterns in corrupted data without requiring explicit modeling of interferences. However, this approach has limitations: unlike robust DSP or adaptive antenna arrays, observable-based techniques rely on the presence of detectable anomalies after the interference has already affected the signal. Finally, the proposed method offers a highly accessible, low-cost, and scalable solution for interference detection in mass-market maritime applications. It enables early detection of interference allowing for safer navigation.