Towards a Faster GNSS Interference Classification: A GRU-Based Approach Using Spectrograms

Iman Ebrahimi Mehr, Gianfranco Caputo, Dario Salza, Maurizio Fantino, and Fabio Dovis

Peer Reviewed

Abstract:	Global Navigation Satellite Systems (GNSS) are critical for Positioning, Navigation, and Timing (PNT) services, but their signals are highly vulnerable to intentional jamming. Traditional interference detection methods, such as spectral analysis and statistical thresholding, struggle to adapt to dynamic interference conditions. Recent advances in Artificial Intelligence (AI) using deep learning techniques, have enabled more robust classification approaches. This study proposes a Gated Recurrent Unit (GRU)-based framework for GNSS interference classification, offering a more lightweight alternative to convolutional neural networks (CNNs), which rely on spectrogram images as inputs. The GRU model directly processes sequential spectrograms, enabling efficient real-time classification with a reduced computational overhead. Experimental validation using datasets collected in a controlled environment, along with an implementation on a low-cost embedded device, demonstrated high classification accuracy and improved efficiency over CNN-based approaches. The findings highlight the potential of AI-driven methods for scalable and real-time GNSS interference monitoring, particularly in resource-constrained environments. Index Terms—GNSS, Monitoring, Interference, jamming, Classification, Machine Learning, Software Defined Radio, Embedded Systems.
Published in:	2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 28 - 1, 2025 Salt Lake Marriott Downtown at City Creek Salt Lake City, UT
Pages:	372 - 380
Cite this article:	Mehr, Iman Ebrahimi, Caputo, Gianfranco, Salza, Dario, Fantino, Maurizio, Dovis, Fabio, "Towards a Faster GNSS Interference Classification: A GRU-Based Approach Using Spectrograms," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 372-380.
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