物联网学报2025，Vol.9Issue(4)：62-76,15.DOI:10.11959/j.issn.2096-3750.2025.00540
基于孪生图卷积神经网络的小样本迁移学习室内指纹定位
Siamese GCN empowered fingerprinting indoor localization using few-shot transfer learning
摘要
Abstract
Radio frequency(RF)-based indoor positioning technology is recognized as one of the important research direc-tions in the sixth generation wireless communication(6G)systems.With the advancement of artificial intelligence(AI),deep learning-based indoor fingerprint localization methods have achieved significant improvements in positioning perfor-mance.However,these methods still face the following challenges,including lengthy RF data collection periods and high annotation costs,which lead to poor environmental generalization capability of existing deep learning algorithms across different scenarios.To address this issue,a few-shot transfer learning indoor fingerprint localization method based on a Siamese graph convolutional network(Siamese GCN)was proposed.The Siamese GCN model was combined with a maximum mean discrepancy-based domain adaptation approach,requiring only a small number of channel state informa-tion samples to be collected in the current environment.Pre-trained network weights from other environments were re-used,significantly reducing data collection and annotation costs in new environments.To validate the effectiveness of the proposed method,real environmental data were collected in two typical indoor scenarios:a laboratory and a corridor.Experimental results demonstrated that the proposed transfer learning method achieved satisfactory localization perfor-mance using only 30%of the labeled samples.关键词
孪生图卷积神经网络/室内定位/迁移学习/信道状态信息Key words
Siamese GCN/indoor localization/transfer learning/channel state information分类
信息技术与安全科学引用本文复制引用
施政,顾浩,黄浩,王禹,夏文超,赵海涛,朱洪波..基于孪生图卷积神经网络的小样本迁移学习室内指纹定位[J].物联网学报,2025,9(4):62-76,15.基金项目
国家自然科学基金资助项目(No.62274096) （No.62274096）
江苏省自然科学基金资助项目(No.BK20240621)The National Natural Science Foundation of China(No.62274096),Natural Science Foundation of Jiangsu Prov-ince(No.BK20240621) （No.BK20240621）
