用对抗性特征对齐提升室内干扰源定位的环境适应性。
Machine and Deep Learning for Indoor UWB Jammer Localization
- 设计对抗性卷积神经网络自动编码器，实现跨环境特征对齐。
- 在新布局下定位误差降至34.67厘米，较基准提升77%。
- 适合智能建筑安防、定位系统抗干扰研究者参考。
超宽带（UWB）定位可实现厘米级精度，但易受干扰攻击，威胁智能建筑中的资产追踪与入侵检测安全。尽管机器学习（ML）和深度学习（DL）已提升标签定位性能，但在单房间内及动态室内布局中定位恶意干扰源仍缺乏研究。本文构建了两个新UWB数据集，分别对应原始与修改后的房间布局，建立全面的ML/DL基线。评估使用多种分类与回归指标。在源数据集上，随机森林达到最高F1-macro得分0.95，XGBoost取得最低均方欧氏误差20.16厘米。然而，在新布局部署时，模型性能严重退化，XGBoost误差增至207.99厘米，体现显著域偏移。为此，提出域对抗性ConvNeXt自动编码器（A-CNT），利用梯度反转层对齐跨域信道冲激响应（CIR）特征。A-CNT将平均欧氏误差降至34.67厘米，较非对抗迁移学习提升77%，较最优基线提升83%，且30厘米内样本比例恢复至0.56。结果表明，对抗特征对齐可实现鲁棒、可迁移的室内干扰源定位。代码与数据集见https://github.com/afbf4c8996f/Jammer-Loc。
原文摘要 · Abstract (English)
Ultra-wideband (UWB) localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in smart buildings. Although machine learning (ML) and deep learning (DL) methods have improved tag localization, localizing malicious jammers within a single room and across changing indoor layouts remains largely unexplored. Two novel UWB datasets, collected under original and modified room configurations, are introduced to establish comprehensive ML/DL baselines. Performance is rigorously evaluated using a variety of classification and regression metrics. On the source dataset with the collected UWB features, Random Forest achieves the highest F1-macro score of 0.95 and XGBoost achieves the lowest mean Euclidean error of 20.16 cm. However, deploying these source-trained models in the modified room layout led to severe performance degradation, with XGBoost's mean Euclidean error increasing tenfold to 207.99 cm, demonstrating significant domain shift. To mitigate this degradation, a domain-adversarial ConvNeXt autoencoder (A-CNT) is proposed that leverages a gradient-reversal layer to align CIR-derived features across domains. The A-CNT framework restores localization performance by reducing the mean Euclidean error to 34.67 cm. This represents a 77 percent improvement over non-adversarial transfer learning and an 83 percent improvement over the best baseline, restoring the fraction of samples within 30 cm to 0.56. Overall, the results demonstrate that adversarial feature alignment enables robust and transferable indoor jammer localization despite environmental changes. Code and dataset available at https://github.com/afbf4c8996f/Jammer-Loc
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