Many critical applications highly depend on Global Navigation Satellite Systems (GNSS) for precise and continuously available positioning and timing information. To warn a GNSS user that the signals are compromised, real-time interference detection is required. Additionally, real-time classification of the interference signal allows the user to select the most effective mitigation methods for the encountered disturbance. A compact proof of concept has been built using commercial off-the-shelf (COTS) components to analyse the jamming detection and classification techniques. It continuously monitors GNSS frequency bands and generates warnings to the user when interference is detected and classified. Various signal spectrum analyses, consisting of kurtosis and power spectral density (PSD) calculations, as well as a machine learning model, are used to detect and classify anomalies in the incoming signals. The system has been tested by making use of a COTS GNSS signal simulator. The simulator is used to generate the upper L-band GNSS signals and different types of interferences. Successful detection and classification is demonstrated, even for interference power levels that do not degrade the performance of a commercial reference receiver.

Many critical applications exhibit a growing dependency on Global Navigation Satellite Systems (GNSS), which has led to GNSS jamming and spoofing becoming an increasing threat to society. The Structural Power Content Analysis (SPCA) algorithm is a pre-despreading, low-complexity, and effective method to detect spoofing events, but in practice it is seen that false alarms are being generated in certain jamming scenarios. To mitigate these effects, alternative filtering techniques are evaluated and tested on both simulated data and publicly available spoofing datasets. Effective false alarm reduction with only a minor degradation in spoofing detection sensitivity is demonstrated.