Improved PSO-extreme learning machine algorithm for indoor localization

WiFi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features. The basic assumption of fingerprinting localization is that the received signal strength indication (RSSI) distance is accord with the location distance. Therefore, how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization. In this paper, a particle swarm optimization-extreme learning machine (PSO-ELM) algorithm is proposed on the basis of the original fingerprinting localization. Firstly, we collect the RSSI of the experimental area to construct the fingerprint database, and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives. Secondly, PSO algorithm is used to improve the bias and weight of ELM neural network, and the global optimal results are obtained. Finally, extensive simulation results are presented. It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor (KNN), Kmeans and Back-propagation (BP) algorithms.