Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning

Abstract

This article proposes the use of Wi-Fi ToF and a deep learning approach to build a cheap, practical, and highly-accurate IPS. To complement that, rather than using the classic geometrical approach (such as multilateration), it uses a more data-driven approach, i.e., the location fingerprinting technique. The fingerprint of a location, in this case, is a set of Wi-Fi ToFs between the target device and an access point (AP). Therefore, the number of APs in the area dictates the set size. The location fingerprinting technique requires a collection of fingerprints of various locations in the area to build a reference database or map. This database or map contains the information used to carry out the main task of the location fingerprinting technique, namely, estimating the position of a device based on its location fingerprint. For that task, we propose using a fully connected deep neural network (FCDNN) model to act as a positioning engine. The model is given a location fingerprint as its input to produce the estimated location coordinates as its output. We conduct an experiment to analyze the impact of the available AP pair in the dataset, from 1 unique AP pair, 2 AP pairs, and more, using WKNN and FCDNN to compare their performance. Our experimental results show that our IPS, DeepIndoor, can achieve an average positioning error or mean square error of 0.1749 m, and root mean square error of 0.5740 m in scenario 3, where 1–10 AP pairs or the raw dataset is used.