发表机构
Wharton Research Data Services, University of Pennsylvania; Department of Electronic, Electrical and Systems Engineering, University of Birmingham（宾夕法尼亚大学沃顿研究数据服务; 伯明翰大学电子、电气与系统工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于主干复用和高效适配的场景自适应RSS定位框架，通过轻量提取器统一异构输入并微调主干，实现跨环境快速收敛，减少训练时间。
AI 中文摘要
基于接收信号强度（RSS）的室内定位因其低成本及与现有无线基础设施的兼容性而受到越来越多的关注。然而，RSS测量对环境变化高度敏感，这使得基于深度学习的定位模型难以在不同物理配置之间进行泛化。受这些挑战的驱动，本文提出了一种基于主干网络复用和高效适配的场景自适应RSS定位框架。所提出的架构由一个轻量级提取器和一个共享主干网络组成，其中提取器将异构RSS输入投影到统一特征空间，主干网络捕获可迁移的定位知识。在适配过程中，复用从源数据集训练的主干网络，同时优化提取器以适应新的数据集配置。此外，引入一个较短的优化阶段，以较低的学习率对主干网络进行轻微微调。在四个数据集上的实验结果表明，与从头训练相比，所提出的训练策略能够实现更快的收敛和更好的适配。此外，所提出的框架在不同数据集配置下减少了训练时间，验证了其在自适应RSS室内定位中的有效性和高效性。
英文摘要
Received signal strength (RSS)-based indoor localization has attracted increasing attention due to its low cost and compatibility with existing wireless infrastructures. However, RSS measurements are highly sensitive to environmental variations, making it challenging for deep learning-based localization models to generalize across different physical configurations. Driven by these challenges, this paper proposes a scenario-adaptive RSS localization framework based on backbone reuse and efficient adaptation. The proposed architecture consists of a lightweight extractor and a shared backbone, where the extractor projects heterogeneous RSS inputs into a unified feature space and the backbone captures transferable localization knowledge. During adaptation, the backbone trained from the source dataset is reused, while the extractor is optimized to adapt to the new dataset configuration. A short optimization stage is further introduced to slightly refine the backbone with a lower learning rate. Experimental results on four datasets demonstrate that the proposed training strategy enables faster convergence and improved adaptation compared with training from scratch. In addition, the proposed framework reduces training time under different dataset configurations, verifying its effectiveness and efficiency for adaptive RSS-based indoor localization.
