The State of the Art of Deep Learning-Based Wi-Fi Indoor Positioning: A Review
Yiruo Lin, Kegen Yu, Feiyang Zhu, Jinwei Bu, Xiaoming Dua
Abstract
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.