Multi-directional weighted interpolation for wi-fi localisation

Abstract

The rise in popularity of unmanned autonomous vehicles (UAV) has created a need for accurate positioning systems. Due to the indoor limitations of the Global Positioning System (GPS), research has focused on other technologies which could be used in this landscape with Wi-Fi localisation emerging as a popular option. When implementing such a system, it is necessary to find an equilibrium between the desired level of final precision, and the time and money spent training the system. We propose Multi-Directional Weighted Interpolation (MDWI), a probabilistic-based weighting mechanism to predict unseen locations. Our results show that MDWI uses half the number of training points whilst increasing accuracy by up to 24%.