Radio frequency interference (RFI) remains a major challenge for Global Navigation Satellite Systems (GNSS), particularly around critical infrastructure such as airports. As these infrastructures rely on stable and uninterrupted GNSS service, it becomes crucial to continuously monitor the spectrum and quickly identify any interference sources.
To address this need, we developed a low-cost network of GNSS sensor nodes capable of detecting, localizing, and characterizing interference sources under real-world conditions. Each sensor records time-synchronized baseband data and sends it to a central processing unit, where a TDOA-based localization algorithm detects the RFI, estimates its position, and characterizes its properties.
A central contribution of this work is the use of geographical constraints to improve the localization process. In many real situations—for example, when interference comes from a device carried inside a moving vehicle—the emitter is naturally limited to a certain area or a known route, such as a highway. By integrating this prior knowledge directly into the localization equations, we show that the system becomes noticeably more robust and, in some cases, can achieve reliable performance with fewer sensors.
To validate the proposed approach, we carried out an extensive measurement campaign including both static and dynamic scenarios. For static tests, when the interferer was located within the sensor network, the system achieved an average horizontal error of around 6 meters. In dynamic scenarios, the accuracy remained on the order of 50 meters. Beyond estimating position, the network was also able to track an unknown moving interferer, examine its signal in time and frequency, estimate its received power, and even infer the speed of the vehicle carrying the device.

Autonomous maritime vessels increasingly rely on Global Navigation Satellite Systems (GNSS) for Positioning, Navigation, and Timing (PNT), yet these systems remain vulnerable to jamming and spoofing attacks that compromise vessel safety. Traditional detection methods analyzing only GNSS signals struggle to distinguish attack types, particularly when sophisticated spoofing maintains valid signal properties.
This work introduces a fundamentally different approach. Rather than examining GNSS signals in isolation, we verify their integrity by checking whether reported vessel positions remain physically plausible against independent Automatic Identification System (AIS) maritime traffic data. The core insight: genuine GNSS measurements should produce trajectories consistent with realistic maritime behavior—vessel density patterns, speed distributions, course stability, and spatial relationships. This reframes interference detection as behavioral consistency verification between independent systems—essential for trustworthy autonomous navigation.
We demonstrate this using synchronized GNSS measurements from the FGI Spoofing and Jamming Dataset and AIS archives from Fintraffic. Neural networks fuse GNSS signal characteristics (magnitude statistics, spectral descriptors, phase-stability indicators) with AIS behavioral attributes (vessel density, speed variance, course stability, spatial dispersion). Rigorous feature engineering reduces 89 descriptors to 44 robust features through leakage-aware filtering.
Results are compelling. GNSS-only detection achieves 88% accuracy with substantial jamming-spoofing confusion. Adding AIS behavioral features improves accuracy to 98.4% (macro-F1: 0.981)—an 85% error reduction with only three misclassifications. The fused system achieves near-perfect jamming detection (F1: 0.995) and significantly enhanced spoofing recognition (F1: 0.969, +11.7 percentage points) by identifying kinematic impossibilities invisible to pure signal analysis.
Scenario-based cross-validation reveals opportunities for further development: performance variations across test scenarios indicate that multi-region data collection and domain adaptation will strengthen operational robustness. The work establishes technical feasibility and demonstrates substantial performance gains of behavioral cross-validation for maritime GNSS integrity monitoring. This approach offers a practical pathway toward resilient navigation systems essential for safe autonomous maritime operations and digital-twin infrastructures.

GNSS interference jamming and spoofing poses a growing threat to positioning, navigation, and timing infrastructure worldwide. Ground-based monitoring networks provide limited spatial coverage and lack the persistent, wide-area observation capability needed to characterize interference at regional or global scale. This paper presents a spaceborne interference monitoring concept implemented in the BEACONSAT mission: Austria's largest satellite platform to date, commissioned by the Austrian Armed Forces (BMLV), and designed to detect and analyze GNSS interference from low Earth orbit with INDALOS as the dedicated onboard payload. A key feature of the system is onboard signal processing: interference detection and classification are performed directly in orbit, reducing downlink bandwidth requirements and enabling near-real-time alerting without ground-in-the-loop latency. The approach exploits a set of GNSS signal observables, encompassing signal quality, cross-signal consistency, and temporal behavior, to detect deviations from nominal conditions. These complementary indicators are fused within a multi-stage processing chain, augmented by an AI-assisted decision layer that improves detection robustness under ambiguous or complex interference scenarios. The methodology has been validated through ground-based experiments under representative jamming and spoofing conditions. The payload needs to cope with the constraints of the space environment: radiation hardness, thermal cycling, vacuum compatibility, and launch mechanical loads. The results demonstrate that ground-validated interference monitoring concepts can be transferred to a satellite-compatible implementation with onboard autonomy. Deployed in orbit, INDALOS would enable continuous, wide-area observation of GNSS disruption activity, supporting earlier threat detection, enhanced situational awareness, and improved resilience of navigation-dependent infrastructure at a global scale.

The increasing threat of intentional radio frequency interference (RFI) in the frequency bands of Global Navigation Satellite Systems (GNSS) drives the demand for establishing a reliable global monitoring system via satellites in the low earth orbit (LEO). The University of Federal Armed Forces Munich has started to build a dedicated small satellite mission with a configurable software-defined radio (SDR) payload that is used for GNSS RFI monitoring applications. The satellite serves as an experimental laboratory in space, operating in the LEO with an altitude of 505 km, following a sun-synchronous orbit with an inclination of 97.42 °. With a payload capacity of 75 kg and a takeoff weight of 200 kg, the platform is designed to support a wide range of advanced experiments. These include broadband communications and internet of things (IOT) applications, radio science experiments using GNSS signals for occultation and reflectometry measurements and monitoring applications of the GNSS RFI situation (Bachmann, et al., 2022). The launch of the satellite platform is scheduled for Q4/2026.
The RFI monitoring payload includes three key components: a zenith-facing antenna to receive GNSS signals from medium earth orbit (MEO) satellites; a nadir-facing antenna to capture signals from ground-based emitters; and a space-qualified GNSS receiver that provides precise position, velocity, and time (PVT) information of the space vehicle, that is needed for the determination of the geolocation of the RFI source. The heart of the payload is the GNSS SDR platform, that digitizes both the received GNSS signals from the zenith antenna and the nadir-facing antenna signals at a defined sampling rate and quantization resolution. Both signal streams are stored within an onboard mass memory before they are downlinked via X-band to a ground station.
Before receiving real data from the single satellite mission, a tool chain has to be developed to efficiently process the obtained signal streams. Therefore, a GNSS signal simulator is used to create realistic ground-based interference scenarios for proper testing of the developed software modules. At the moment, the tool chain has the capability to detect and geolocate basic kinds of RFI sources like continuous wave or pseudo-like waveforms. As the attack types become more advanced over time, the processing capabilities of the tool chain must increase accordingly to detect and provide reliable location information of realistic emitter sources. Therefore, the GNSS signal simulator was configured in such a way to generate spoofing-like emitter signals in a static spoofed position scenario. This was done by applying additional range and Doppler offsets to each of the authentic GNSS signals generated. The needed modifications in the processing chain are presented, as only the propagation-dependent range and Doppler information of the spoofing signal are needed for geolocation.

References:
Bachmann, J., Kinzel, A., Porcelli, F., Schmidt, A., Schwarz, R., Hofmann, C., Knopp, A. (2022). SeRANIS: In-Orbit-Demonstration von Spitzentechnologie auf einem Kleinsatelliten. Deutscher Luft- und Raumfahrtkongress, (S. 12). Dresden.