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

arXiv: https://arxiv.org/abs/2510.20666 · HTML: https://mlsanity.com/paper/2510.20666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fusion of Probability Density Functions](https://mlsanity.com/paper/2202.11633/) (2022)
- [Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks](https://mlsanity.com/paper/2007.11994/) (2020)
- [Jamming Source Localization Using Augmented Physics-Based Model](https://mlsanity.com/paper/2212.08097/) (2022)
- [Using Mobile Phones for Participatory Detection and Localization of a GNSS Jammer](https://mlsanity.com/paper/2305.02038/) (2023)
- [Graph Neural Networks for Jamming Source Localization](https://mlsanity.com/paper/2506.03196/) (2025)

## Abstract (arXiv)

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.