Wi-Fi Fingerprint Indoor Localization by Semi-Supervised Generative Adversarial Network

被引:2
作者
Yoo, Jaehyun [1 ]
机构
[1] Sungshin Womens Univ, Sch AI Convergence, 34 da-gil 2,Bomun Ro, Seoul 02844, South Korea
关键词
generative adversarial network; indoor localization; semi-supervised learning; Wi-Fi fingerprint; MAP;
D O I
10.3390/s24175698
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Wi-Fi fingerprint indoor localization uses Wi-Fi signal strength measurements obtained from a number of access points. This method needs manual data collection across a positioning area and an annotation process to label locations to the measurement sets. To reduce the cost and effort, this paper proposes a Wi-Fi Semi-Supervised Generative Adversarial Network (SSGAN), which produces artificial but realistic trainable fingerprint data. The Wi-Fi SSGAN is based on a deep learning, which is extended from GAN in a semi-supervised learning manner. It is designed to create location-labeled Wi-Fi fingerprint data, which is different to unlabeled data generation by a normal GAN. Also, the proposed Wi-Fi SSGAN network includes a positioning model, so it does not need a external positioning method. When the Wi-Fi SSGAN is applied to a multi-story landmark localization, the experimental results demonstrate a 35% more accurate performance in comparison to a standard supervised deep neural network.
引用
收藏
页数:12
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