The rise in popularity of unmanned autonomous vehicles (UAV) has created a need for accurate positioning systems. Due to the indoor limi- tations of the Global Positioning System (GPS), research has focused on other technologies which could be used in this landscape with Wi-Fi local- isation emerging as a popular option. When implementing such a system, it is necessary to find an equilibrium between the desired level of final pre- cision, 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%.