An Experimental Study of Deep Learning Approach for Indoor Positioning System Using WI-FI System

Global navigation satellite system (GNNS) is known for its capability to detect the whereabouts of any desired target such as vehicles and places. However, there is some disadvantage of these technologies as it can only get a precise location outside of the building because as the signal goes to indoor building, the signal becomes weaker due to attenuation. The Wi-Fi systems are the best alternative to GNNS in an indoor environment since the architecture is massively deployed in many recent buildings. However, Wi-Fi also has its disadvantage where its signal is non-linear due to various factors such as multipath and signal blockage indoors thus limiting the system accuracy. In this paper, a deep learning approach with standalone Wi-Fi technologies will be used to have a precise indoor positioning by using the fingerprinting method. The overall result shows that the average distance error between actual and estimated location is 20-cm and the highest error is 62-cm in an experimental area of 180-cm and 120-cm in x and y axis. This shows that deep learning is a possible method to have accurate and precise indoor positioning.