融合物理模型与深度学习，提升城市环境下的干扰源定位精度与可信度。
Bayesian Jammer Localization with a Hybrid CNN and Path-Loss Mixture of Experts
- 用物理路径损耗模型与CNN结合，通过对数线性池化融合特征。
- 定位误差随训练点增加而降低，不确定性在干扰源附近集中。
- 适合需要高可信度定位的智能交通与反干扰系统应用。
全球导航卫星系统（GNSS）信号易受干扰，尤其在城市环境中，多径效应和遮挡导致接收功率失真。以往数据驱动方法虽能实现合理定位，但对接收信号强度（RSS）场重建效果不佳，因缺乏空间上下文。本文提出一种混合贝叶斯专家模型框架，通过对数线性池化融合物理路径损耗（PL）模型与卷积神经网络（CNN）。PL专家保障物理一致性，CNN利用建筑高度图捕捉城市传播特性。采用拉普拉斯近似进行贝叶斯推断，获得干扰源位置与RSS场的后验不确定性。基于城市射线追踪数据的实验表明，随着训练样本增多，定位精度提升，不确定性下降；且不确定性在干扰源及城市峡谷区域集中，反映传播敏感性增强。
原文摘要 · Abstract (English)
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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