Abstract: Under the complex indoor wireless local area networks(WLAN) environment,the improved motley keenan(MK) model and the weighted K nearest neighbors(WKNN) positioning methods are used to improve the performance of location.Firstly,the indoor propagation models and the improved MK model are introduced.Then,the fingerprint positioning methods and the basic theories of those two positioning algorithms are illustrated.According to the experimental data,the positioning performance and error sources of those methods are analyzed.Finally,the combined hybrid positioning method between MK model and WKNN was proposed,and the simulation and analysis were made.Simulation results show that the proposed algorithm improves the accuracy and reduces the positioning error.

Key words: Wireless local area networks,Indoor positioning,Motley Keenan model,Weighted K nearest neighbors,Hybrid positioning