Research On Single Access Point WiFi Indoor Positioning Technology Based On Channel State Information

With the rapid development of wireless communication technology and the area covered by WIFI becoming much wider,location-based services through WIFI are catching more and more attention.Early indoor wireless location is mainly based on RSS(Received Signal Strength).However,owing to complex environments,RSS has a high fluctuation over time which intensifies serious multipath effect,makes the propagation of wireless signal more complex,and leads to RSS unstable.Recent years,researchers have shifted their focus to CSI(Channel State Information).Comparing with RSS integrating received signals intensity in layer,CSI is able to express more finer-grained information,reflect multipath effect in the environments in a better way and provide more accurate location.Nowadays,there are lots of CSI-based positioning systems.Although there are many existing positioning systems which can achieve indoor location,they still have more or less flaws.Therefore,researchers are eager to explore new indoor location system.This article studies CSI-based single access point indoor location technology,which mainly includes transmission distance estimation algorithm based on CSI phase information of all channels in the WIFI 2.4 GHz band and target location algorithm based on 4×4 MIMO.Firstly,in this paper,the WIFI 2.4 GHz frequency hopping technique is used to collect the CSI phase information of all the WiFi channels.The Discrete Fourier Transform and the sparse representation based indoor 2.4 GHz band multipath separation methods are studied and compared of.According to simulation results,the former one is better than the later one.Secondly,the transmission distance estimation algorithm based on CSI phase information of all channels in the WIFI 2.4 GHz band is studied.A transmission distance estimation algorithm based on the phase difference of the central carrier CSI between the adjacent channel in the 2.4GHz band is proposed.In order to reduce the error of measurement distance caused by phase ambiguity,this paper uses K-means clustering method to remove outliers and replace them with clustering centers,so as to achieve the purpose of removing the larger measurement distance.The experimental results show that under the same conditions,the error of the estimation algorithm proposed in this paper is relatively small,so it is better than the estimation algorithm based on the Chinese remainder theorem.The minimum error of this transmission distance estimation algorithm is 0.09m,and the average error is 0.47mFinally,in this paper,based on the measured distance between 16 pairs of transmit and receive antennas of a 4×4 MIMO antenna array,an optimization algorithm for calculating the target position using the geometric constraint relationship between antennas is studied.The simulation results of the measurement points in the laboratory show that the algorithm finally achieves a high-precision positioning with a minimum error of 0.3m and an average error of 0.84m.