给路由器加注意力机制，定位精度提升超30%
Attending to Routers Aids Indoor Wireless Localization
- 引入路由器注意力机制，动态加权各路由器信号
- 在公开数据集上比基准模型准确率高30%以上
- 适合需要高精度室内定位的场景
基于机器学习的无线定位技术在使用Wi-Fi信号时，仍面临跨环境性能难以突破的挑战。主要问题在于现有算法在聚合多路由器信息时未能合理加权，导致收敛不佳、精度下降。受传统加权三角测量启发，本文提出将注意力机制引入路由器层面，使每个路由器的贡献根据其相关性动态调整。通过在标准机器学习定位架构中加入注意力层，实验表明该方法显著提升了整体性能。在多个开源数据集上的评估显示，该方法相比基准模型定位准确率提升超过30%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。