Received Signal Strength Based Indoor Localization using ISODATA and MK-ELM Technique

Abstract

With the development of the smart city, indoor localization has received much attentions. In this paper, a novel received signal strength (RSS) based fingerprint localization algorithm was proposed by utilizing iterative self-organizing data analysis techniques algorithm (ISODATA) and multiple kernel extreme learning machine (MK-ELM) technique. In the offline phase, the measurement label of each RSS measurement training data is given after using ISODATA clustering. And then the measurement-label training set and the measurement-position training subsets can be formed. Next, using the MK-ELM algorithm, the measurement classification function and the position regression sub-function can be learned by the measurement-label training set, measurement-position training subset respectively. In the online phase, the classification result of the obtained RSS measurements is obtained firstly. Then the corresponding regression function is chosen for the final position estimation. The experimental results illustrated its performance with respect to position estimation and computational complexity.