The State of the Art of Deep Learning-Based Wi-Fi Indoor Positioning: A Review

被引：6

作者：

Lin, Yiruo ^{[1
]}

Yu, Kegen ^{[1
]}

Zhu, Feiyang ^{[1
]}

Bu, Jinwei ^{[2
]}

Dua, Xiaoming ^{[1
]}

机构：

[1] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Peoples R China

[2] Kunming Univ Sci & Technol, Fac Land Resources Engn, Kunming 650093, Peoples R China

来源：

IEEE SENSORS JOURNAL | 2024年 / 24卷 / 17期

基金：

中国国家自然科学基金;

关键词：

Deep learning (DL); machine learning (ML); review; Wi-Fi indoor positioning; SWARM OPTIMIZATION; LOCALIZATION; ALGORITHM; SYSTEM; UWB; NETWORK; RSSI; CLASSIFICATION; FINGERPRINTS; FRAMEWORK;

D O I：

10.1109/JSEN.2024.3432154

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：

Wi-Fi positioning has drawn great attention in the field of indoor positioning, due to its low cost, easy deployment, and large positioning range. However, the Wi-Fi signal is highly volatile due to multipath propagation in indoor environments, which seriously affects the positioning accuracy. Deep learning (DL), a subset of machine learning (ML), is particularly suited to handle the effect of signal fluctuation for Wi-Fi indoor positioning, due to its good nonlinear mapping and good fault tolerance. This article presents a timely, systematic, and comprehensive review on DL-based Wi-Fi indoor positioning. Specifically, this review mainly focuses on the basic theory of Wi-Fi indoor positioning, the organization of the latest literatures on DL-based Wi-Fi indoor positioning, and statistical analysis on the function of DL models in Wi-Fi positioning, measurement data for the DL models, the source of the test dataset, and the positioning accuracy under each DL model. We also present a generalization of the process of building a Wi-Fi positioning model through DL. Furthermore, we discuss the challenges of DL-based Wi-Fi indoor positioning and the trends of its future development.

引用

页码：27076 / 27098

页数：23

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