Real-Time jamming detection using windowing and hybrid machine learning models for pre-saturation alerts

This paper proposes a new deep learning and machine learning model for detecting deception and suppression jamming in Ublox-M8T receivers operating under GNSS interference. This solution employs XGBoost for real-time classification of jamming signals, implemented on an STM32H743 microcontroller to ensure ultra-low latency, making it suitable for navigation in various environments. This work's key contribution is integrating a windowing mechanism for pre-saturation alerts and early activation of jamming detection which enhances system reliability by distinguishing between high-credibility and low-credibility GNSS data under static and dynamic jamming conditions. To validate the model, a series of experiments were conducted using a software-defined radio transmitter to simulate jamming scenarios. Genuine GNSS and jamming signals were collected under controlled conditions, and the data were pre-processed through feature normalization, correlation analysis, and feature selection based on importance in the mentioned systems. The XGBoost classifier, trained and tested on this processed dataset, achieved a detection rate of 99.97%, a precision of 99.94%, and a Matthews correlation coefficient of 0.9992, with an average prediction time of only 20 microseconds per sample in the implemented mode, making it an excellent choice for real-time systems. Additionally, the windowing mechanism enhances system performance by proactively initiating countermeasures before reaching saturation, ensuring continuous operation during high-intensity jamming attacks.