A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks

An indoor localization system using the affinity propagation (AP) clustering algorithm and the particle swarm optimization based artificial neural network (PSO-ANN) to reduce the positioning error and shorten the prediction time is proposed.

Abstract

Wi-Fi based indoor localization system has attracted considerable attention due to the growing need for location based service (LBS) and the rapid development of mobile phones. However, most existing Wi-Fi based indoor positioning systems suffer from the low accuracy due to the dynamic variation of indoor environment and the time delay caused by the time consumption to provide the position. In this paper, we propose an indoor localization system using the affinity propagation (AP) clustering algorithm and the particle swarm optimization based artificial neural network (PSO-ANN). The clustering technique is adopted to reduce the maximum location error and enhance the prediction performance of PSO-ANN model. And the strong learning ability of PSO-ANN model enables the proposed system to adapt to the complicated indoor environment. Meanwhile, the fast learning and prediction speed of the PSO-ANN would greatly reduce the time consumption. Thus, with the combined strategy, we can reduce the positioning error and shorten the prediction time. We implement the proposed system on a mobile phone and the positioning results show that our algorithm can provide a higher localization accuracy and significantly improves the prediction speed.