CO-LEELM: Continuous-Output Location Estimation Using Extreme Learning Machine

Indoor positioning is a key technology enabler for various smart systems that require location-based optimization and automation. In this paper, we present CO-LEELM, a continuous-output location fingerprinting method that combines two existing location fingerprinting methods to produce better accuracy in a dynamic environment where training data and reference devices are sparsely-distributed. The proposed method incorporates the use of Extreme Learning Machine (ELM) to improve the training speed which is a crucial factor that affects the scalability of the method.