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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 认领 被引量：1

Siamese GCN empowered fingerprinting indoor localization using few-shot transfer learning
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摘要 基于射频信号的室内定位技术是第六代无线通信系统中的重要研究方向之一。随着人工智能的发展,基于深度学习的室内指纹定位方法在定位性能上得到了显著提升。然而,这类方法仍面临以下挑战,包括射频数据采集时间长、标注成本高,导致现有...展开更多 Radio frequency(RF)-based indoor positioning technology is recognized as one of the important research directions in the sixth generation wireless communication(6G)systems.With the advancement of artificial intelligence(AI),deep learning-based indoor fingerprint localiz...MORE Radio frequency(RF)-based indoor positioning technology is recognized as one of the important research directions 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 performance.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 information samples to be collected in the current environment.Pre-trained network weights from other environments were reused,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 performance using only 30%of the labeled samples.FEWER
作者 施政 顾浩 黄浩 王禹 夏文超 赵海涛 朱洪波 SHI Zheng;GU Hao;HUANG Hao;WANG Yu;XIA Wenchao;ZHAO Haitao;ZHU Hongbo(School of Communications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;School of Integrated Circuits,Southeast University,Nanjing 211189,China;Jiangsu Province Key Laboratory of Wireless Communication and Internet of Things,Nanjing 210003,China;School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
机构地区 南京邮电大学通信与信息工程学院 东南大学集成电路学院 江苏省无线通信与物联网重点实验室 南京邮电大学物联网学院
出处 《物联网学报》 2025年第4期62-76,共15页 Chinese Journal on Internet of Things
基金 国家自然科学基金资助项目(No.62274096) 江苏省自然科学基金资助项目(No.BK20240621)。
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 Siamese GCN indoor localization transfer learning channel state information
分类号 TP183 [自动化与计算机技术—控制理论与控制工程] TN92 [自动化与计算机技术—控制科学与工程]
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物联网学报
2025年 第4期
科创助手
论文智评
维普砚承
维普斟知

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