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

J Sormayli1, M Darvishi1, K Zarrinnegar1

  • 1Department of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.

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Summary

This study introduces an advanced machine learning model for detecting Global Navigation Satellite System (GNSS) interference in Ublox-M8T receivers. The solution offers high-accuracy, real-time jamming detection with ultra-low latency for reliable navigation systems.

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Area of Science:

  • Navigation Systems Engineering
  • Machine Learning Applications
  • Signal Processing

Background:

  • Global Navigation Satellite System (GNSS) receivers, like the Ublox-M8T, are vulnerable to interference, including deception and suppression jamming.
  • Jamming can degrade navigation accuracy and reliability, posing risks in safety-critical applications.
  • Existing detection methods may lack the speed or accuracy needed for real-time countermeasures.

Purpose of the Study:

  • To develop and validate a novel deep learning and machine learning model for detecting deception and suppression jamming in Ublox-M8T receivers.
  • To implement a real-time, ultra-low latency solution suitable for various navigation environments.
  • To enhance GNSS system reliability through early jamming detection and pre-saturation alerts.

Main Methods:

  • Proposed a new model integrating XGBoost for real-time jamming signal classification.
  • Implemented the solution on an STM32H743 microcontroller for ultra-low latency.
  • Incorporated a windowing mechanism for pre-saturation alerts and early jamming detection.
  • Conducted experiments using software-defined radio to simulate jamming scenarios.
  • Pre-processed collected GNSS and jamming data via feature normalization, correlation analysis, and feature selection.

Main Results:

  • The XGBoost classifier achieved a 99.97% detection rate and 99.94% precision.
  • A Matthews correlation coefficient of 0.9992 was obtained, indicating high classification accuracy.
  • Average prediction time was only 20 microseconds per sample, demonstrating suitability for real-time applications.
  • The windowing mechanism effectively distinguished between high-credibility and low-credibility GNSS data under static and dynamic jamming.

Conclusions:

  • The proposed XGBoost model with a windowing mechanism provides highly accurate and fast detection of GNSS jamming.
  • The implemented solution ensures enhanced system reliability and continuous navigation operation during interference.
  • This approach offers a robust defense against jamming attacks, improving the trustworthiness of GNSS data.