Bayesian Jammer Localization with a Hybrid CNN and Path-Loss Mixture of Experts

• URL: http://arxiv.org/abs/2510.20666v1
• Date: Thu, 23 Oct 2025 15:45:45 GMT
• Title: Bayesian Jammer Localization with a Hybrid CNN and Path-Loss Mixture of Experts
• Authors: Mariona Jaramillo-Civill, Luis González-Gudiño, Tales Imbiriba, Pau Closas,
• Abstract summary: We propose a hybrid mixture-of-experts framework that fuses a physical path-loss model and a convolutional neural network.<n>Experiments on urban ray-tracing data show that localization accuracy improves and uncertainty decreases with more training points.
• Score: 10.211561241281565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Global Navigation Satellite System (GNSS) signals are vulnerable to jamming, particularly in urban areas where multipath and shadowing distort received power. Previous data-driven approaches achieved reasonable localization but poorly reconstructed the received signal strength (RSS) field due to limited spatial context. We propose a hybrid Bayesian mixture-of-experts framework that fuses a physical path-loss (PL) model and a convolutional neural network (CNN) through log-linear pooling. The PL expert ensures physical consistency, while the CNN leverages building-height maps to capture urban propagation effects. Bayesian inference with Laplace approximation provides posterior uncertainty over both the jammer position and RSS field. Experiments on urban ray-tracing data show that localization accuracy improves and uncertainty decreases with more training points, while uncertainty concentrates near the jammer and along urban canyons where propagation is most sensitive.

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