Jammer Source Localization with Federated Learning

Mariona Jaramillo Civill, Peng Wu, Andrea Nardin, Tales Imbiriba, and Pau Closas

Peer Reviewed

Abstract:	Global Navigation Satellite System (GNSS) signals are increasingly vulnerable to jamming, disrupting critical applications like autonomous navigation and aviation. Traditional jammer localization relies on centralized data processing, raising privacy concerns. This work proposes a federated learning (FL) framework for privacy-preserving jammer localization using crowdsourced received signal strength (RSS) measurements. We explore three models: a neural network (NN) for initial localization, a path-loss model (PL), and an augmented physics-based model (APBM) combining both PL and NN models. Evaluations in open-sky, suburban and urban environments show that PL and APBM outperform a non-FL baseline in open-sky and suburban settings, while urban scenarios remain challenging due to multipath and shadowing. In addition, we analyze the impact of client distribution, observation density, and measurement noise on localization accuracy. Index Terms—GNSS interference, jammer localization, federated learning, privacy-preserving machine learning.
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:	362 - 371
Cite this article:	Civill, Mariona Jaramillo, Wu, Peng, Nardin, Andrea, Imbiriba, Tales, Closas, Pau, "Jammer Source Localization with Federated Learning," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 362-371.
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