基于域自适应的Wi-Fi指纹设备无关室内定位模型

赵增华; 童跃凡; 崔佳洋

doi:10.11959/j.issn.1000-436x.2022069

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基于域自适应的Wi-Fi指纹设备无关室内定位模型

学术论文 | 更新时间：2024-06-05

- 基于域自适应的Wi-Fi指纹设备无关室内定位模型
- Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation
- 通信学报 2022年43卷第4期页码：143-153
- 作者机构：
  
  天津大学智能与计算学部，天津 300350
- 作者简介：
  
  [ "赵增华（1974- ），女，河南南乐人，天津大学副教授、硕士生导师，主要研究方向为移动和普适计算、室内定位、水下无线网络、软件定义无线网络、计算机网络协议和系统等" ]
  [ "童跃凡（1999- ），女，福建莆田人，天津大学硕士生，主要研究方向为室内定位等" ]
  [ "崔佳洋（1998- ），男，河北唐山人，天津大学硕士生，主要研究方向为室内定位等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61972283)
- DOI：10.11959/j.issn.1000-436x.2022069
  中图分类号： TP393
- 网络首发：2022-04，
  
  纸质出版：2022-04-25
- 稿件说明：
移动端阅览
赵增华, 童跃凡, 崔佳洋. 基于域自适应的Wi-Fi指纹设备无关室内定位模型[J]. 通信学报, 2022,43(4):143-153.

Zenghua ZHAO, Yuefan TONG, Jiayang CUI. Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation[J]. Journal on Communications, 2022, 43(4): 143-153.
赵增华, 童跃凡, 崔佳洋. 基于域自适应的Wi-Fi指纹设备无关室内定位模型[J]. 通信学报, 2022,43(4):143-153. DOI： 10.11959/j.issn.1000-436x.2022069.

Zenghua ZHAO, Yuefan TONG, Jiayang CUI. Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation[J]. Journal on Communications, 2022, 43(4): 143-153. DOI： 10.11959/j.issn.1000-436x.2022069.

摘要

基于Wi-Fi指纹定位方法在大规模实际应用中存在设备多样性问题，定位精度受到极大影响。提出了一种设备无关的 Wi-Fi 指纹室内定位模型 DeviceTransfer。该模型基于深度学习的域自适应理论，把智能手机的设备类型作为域，通过对抗训练来提取任务相关而设备无关的Wi-Fi数据特征，并把学习到的源域位置信息迁移到目标域上。采用预训练和联合训练来提高模型训练的稳定性并加快收敛。在教学楼和商场2个真实场景中，使用 4 台不同型号的智能手机验证模型的性能。实验结果表明，DeviceTransfer 能够有效提取设备无关的Wi-Fi数据特征。只使用一台手机在参考点采集Wi-Fi指纹，使用其他型号手机在线定位也能获得较高的定位精度，降低了定位成本。

Abstract

In real-world large-scale deployments of indoor localization

Wi-Fi fingerprinting approaches suffer from device diversity problem which impacts the localization accuracy significantly.A device-independent Wi-Fi fingerprint indoor localization model DeviceTransfer was proposed.Based on the domain adaptation theory of deep learning

the device type of the smartphone was taken as the domain

the task-related and device-independent Wi-Fi data features were extracted through adversarial training

and the learned source domain location information was transferred to the target domain.Pre-training and joint training were employed to improve model training stability and to accelerate convergence.The performance of DeviceTransfer was evaluated using four types of smartphones in two real-world indoor environments: a school building and a shopping mall.The experimental results show that DeviceTransfer effectively extracts device-independent Wi-Fi fingerprint features.Using only one type of phone to collect Wi-Fi fingerprints

online localization using other types still achieves high localization accuracy

thus reducing localization cost significantly.

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references

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