RSS Missing Value Estimation with Generative Adversarial Networks Model

被引：0
|
作者：
Ren X.-Q. [1 ]
Own C.-M. [1 ]
机构：
[1] College of Intelligence and Computing, Tianjin University, Tianjin
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2021年 / 44卷 / 01期
关键词：
Generative adversarial network; Indoor positioning system; Received signal strength fingerprint database;
D O I：
10.13190/j.jbupt.2019-210
中图分类号：
学科分类号：
摘要：
Wireless-fidelity(Wi-Fi)positioning is currently the mainstream method in indoor positioning, and the construction of fingerprint database is the key to Wi-Fi positioning system. However, the received signal strength(RSS)value in the fingerprint database will be changed with the variability of the indoor environment, and it is usually need to constantly re-measure the value in the fingerprint database, which leads to high cost and long time, especially in the dynamic environment with large positioning area. To address this problem, the adaptive context generative adversarial networks model is proposed. The model only needs to measure part of RSS fingerprints, and then learn the RSS fingerprints distribution to finally predict the missing fingerprint at a specific location. Simulation shows that the accuracy of indoor positioning is significantly improved, and the labor cost is greatly reduced. © 2021, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
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页码：26 / 31
页数：5
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