An indoor positioning algorithm based on geometry and RSS clustering

Abstract

With the WiFi becoming more and more popular over the last few years, the study on the technology of indoor positioning based on WiFi is given more and more attention. Being specific to the problem that not all the clustered reference points(RPs) of the traditional clustering algorithm have geometric proximity in indoor positioning, this paper puts forward the affinity propagation presentation(AP) which bases on the combination of geometry and received signal strength (RSS). Being different from the traditional clustering algorithm, this kind of algorithm can endow the RPs with geometrical and endow the RSS with corresponding weight according to the positioning conditions, it can use the features of the geometry and RSS to restrain the sort of RPs, and that can make the sort of RPs more reasonable. After that it will use k-Nearest Neighbor Algorithm(KNN) to have accurate positioning, which not only can reduce the calculation, but also improve the localization accuracy. Simulation shows that, this kind of algorithm possesses the advantage of fast location and higher localization accuracy.