Adaptive Indoor Localization with Wi-Fi Based on Transfer Learning

被引：0
|
作者：
Hu, Anmin [1 ]
Zhang, Lijun [1 ]
机构：
[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
来源：
2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW) | 2019年
基金：
中国国家自然科学基金;
关键词：
Wi-Fi; indoor localization; machine learning; transfer learning;
D O I：
10.1109/icce-tw46550.2019.8991969
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Many approaches used in The Wi-Fi based indoor location system (WILS) typically assume the distribution of the signal strength data is time invariant. However, the assumption does not hold in real world, which degrades the location accuracy. We propose an algorithm that can adjust the distribution of training data by mixing a fraction of new data. Experimental results show that our algorithm can greatly improve the localization accuracy and reduce a great amount of the calibration effort
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页数：2
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