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.
Highlights
In terms of the technologies applied in indoor positioning, one of the most popular methods is WiFi
These results reveal that systems using Channel state information (CSI) signals could achieve more stable and accurate positioning performances than those using Received signal strength (RSS)
The results showed that the system based on 1D-Convolutional neural network (CNN) and CSI data reaches the maximum error of 0.92 m with the probability of 99.97%
Summary
In terms of the technologies applied in indoor positioning, one of the most popular methods is WiFi. Proposing a sub-metre level accuracy WiFi-based navigation system is still a research challenge. The problem with this type of system is the high-dimensional data. To accurately locate the targeted person or object in the indoor environment, the system needs to analyse the signals from hundreds of nearby WiFi APs. Traditional machine learning methods are slow in dealing with such high-dimensional dataset. Recent systems leverage these big data by applying deep learning, a relatively new machine learning approach to provide new representation of input data. Other than its capability of extracting hierarchical information from discrete input data, deep learning could generate accurate position estimation directly. In current WiFi-based indoor positioning systems, deep learning could be used as a feature extraction method, or, as a positioning prediction method
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