Extreme learning machine with dead zone and its application to WiFi based indoor positioning

Extreme learning machine (ELM) as an emergent technology has shown its good performance in regression applications as well as in large dataset classification applications. It has been broadly embedded in many applications due to its fast speed of computation and accuracy. How to make good use of machine learning techniques in Indoor Positioning System (IPS) is a hot research topic in recent years. Some existing IPSs have already adopted ELM, but it suffers from signal variation and environmental dynamics in indoor settings. In this paper, extreme learning machine with dead zone (DZ-ELM) is proposed to address this problem. The consistency of this approach should be applied is studied. Simulations are also conducted to compare the performance of DZ-ELM and ELM. Lastly, real-world experimental results show that the proposed algorithm can not only provide higher accuracy but also improve the repeatability of IPSs.