A survey of deep learning approaches for WiFi-based indoor positioning. Issue 2 (3rd April 2022)

• Record Type: Journal Article
• Authors: Feng, Xu; Nguyen, Khuong An; Luo, Zhiyuan
• Is Part Of: Journal of information and telecommunication. Volume 6:Issue 2(2022)
• Page Start: 163
• Page End: 216
• Publication Date: 2022-04-03
• Subjects: Deep learning -- neural network -- WiFi fingerprinting; Telecommunication -- Periodicals; Information technology -- Periodicals; 621.382
• DOI: 10.1080/24751839.2021.1975425
• Languages: English
• ISSNs: 2475-1839

Abstract

Abstract : One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments.