{"ID":2851250,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20666","arxiv_id":"2510.20666","title":"Bayesian Jammer Localization with a Hybrid CNN and Path-Loss Mixture of Experts","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.20666","url_pdf":"https://arxiv.org/pdf/2510.20666v1","authors":"[\"Mariona Jaramillo-Civill\",\"Luis González-Gudiño\",\"Tales Imbiriba\",\"Pau Closas\"]","published":"2025-10-23T15:45:45Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.SP\"]","methods":"[\"Mixture of Experts\",\"Convolutional Neural Network\"]","has_code":false}