Modern machine learning-based wireless localization using Wi-Fi signals continues to face significant challenges in achieving groundbreaking performance across diverse environments. A major limitation is that most existing algorithms do not appropriately weight the information from different routers during aggregation, resulting in suboptimal convergence and reduced accuracy. Motivated by traditional weighted triangulation methods, this paper introduces the concept of attention to routers, ensuring that each router's contribution is weighted differently when aggregating information from multiple routers for triangulation. We demonstrate, by incorporating attention layers into a standard machine learning localization architecture, that emphasizing the relevance of each router can substantially improve overall performance. We have also shown through evaluation over the open-sourced datasets and demonstrate that Attention to Routers outperforms the benchmark architecture by over 30% in accuracy.

翻译：基于Wi-Fi信号的现代机器学习无线定位方法在不同环境中实现突破性性能仍面临重大挑战。一个主要局限在于，现有大多数算法在聚合过程中未能对不同路由器的信息进行适当加权，导致收敛效果欠佳且精度降低。受传统加权三角定位方法的启发，本文引入了路由器注意力机制，确保在聚合多路由器信息进行三角定位时，各路由器的贡献具有差异化权重。我们通过将注意力层集成到标准机器学习定位架构中证明，强调各路由器的相关性能够显著提升整体性能。基于开源数据集的评估结果表明，路由器注意力机制在精度上超越基准架构超过30%。