Most existing indoor positioning systems (IPSs) adopt the WiFi fingerprinting technique to localize WiFi enabled mobile devices. In this paper, we improve the WiFi fingerprinting based IPS by efficiently combining the universal Kriging (UK) interpolation method, K nearest neighbor (KNN) and naive Bayes classifier (NBC). Specially, the proposed IPS takes into account the comprehensive features of received signal strengths (RSSs) by adopting the UK method and area partitioning, and further mitigates the boundary effect (which deteriorates the localization accuracy when dealing with mobile devices near the boundary) by virtually augmenting the space boundary. Finally, NBC and weighted KNN (WKNN) are integrated to work with the interpolated fingerprint database. Extensive experiments are carried out in our lab, and show that the proposed IPS with 28 observation points is able to achieve the average positioning error of 1.265m, which is less by 46.6% than the counterparts of the traditional IPS with 28 observation points and is even comparable to the traditional IPS with 112 observation points.