基于域自适应的Wi-Fi指纹设备无关室内定位模型

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

Abstract

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.

Key words: device diversity, Wi-Fi fingerprinting localization, indoor localization, domain adaptation, deep learning

CLC Number:

TP393

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.

Figures/Tables 10

References 35

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Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation

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