Research On Indoor Fingerprint Location Algorithm Based On CSI

With the continuous maturation of Wi-Fi technology and the extensive deployment of wireless devices indoors,the indoor positioning technology based on wireless signals has received more and more attention.Among them,location-based fingerprinting technology has become the focus of research in indoor location because of its low cost,wide application range and no additional hardware support.In recent years,due to the hardware devices continue to support the physical layer channel state information acquisition,the fingerprint positioning technology based on CSI(Channel State Information)provides a new way of thinking for indoor positioning research.CSI provides finer channel state information and is less affected by multipath effects,so its signal characteristics are more stable than RSSI.Therefore,the emergence of CSI leads a new direction for fingerprint positioning technology research and development indoors.At present,most of the CSI-based fingerprint positioning systems only take the channel amplitude information as the fingerprint feature of the position in the sampling stage,which is not perfect for the analysis of the channel state;in addition,in the positioning phase they usually do not consider the positioning environment relative to the baseline environment changes,and only use existing algorithms for feature matching,which results a larger positioning error,so positioning accuracy still need to improve.In terms of the shortcomings of the fingerprint positioning systems,this paper analyzes the CSI characteristics of the channel and extracts the reliable phase information by the mathematical method,which increases available information in the positioning phase.At the same time,two positioning solutions are proposed for the different indoor environment:First,when the channel is stable in indoor environment,a fingerprint localization algorithm based on correlation coefficient is proposed.The algorithm uses the correlation coefficient between the nodes to obtain the matching probability between two sites or two states by verifying the relationship between the correlation coefficient and the position of the nodes.Finally,the coordinates of the maximum and the second point of the matching probability are used to determine the final position of the measured point.Compared with the Bayes algorithm,this method reduces the complexity of the probability calculation and realizes the more convenient position estimation.Secondly,when the localization environment changes greatly with respect to the sampling environment,an adaptive correction model localization algorithm is proposed.In the case of channel instability,the algorithm reflects the change of the positioning environment relative to the reference environment by introducing a perceptual factor PEM(Percentage of nonzero Elements)based on the sampling environment,and then uses the established adaptive correction model to update the fingerprint database.Finally,the position estimation of the measured points is achieved by the improved AWKNN(AP weighted and distanced weighted K Nearest Neighbor)algorithm.The experimental results show that when the channel is stable in indoor environment,the positioning algorithm based on the correlation coefficient is used to reduce the average positioning error of the measured points from 1.18 m to 0.75 m by introducing the phase information.Compared with the Bayes algorithm,the correlation coefficient algorithm reduces the computational complexity greatly and improve the positioning performance of the system.When the channel is unstable,the adaptive correction model positioning system improves the accuracy rate by 67% and 25% respectively compared with the previous fingerprint positioning system FIFS and CSI-MIMO.And adaptive correction model algorithm achieves the adaptive positioning of the system in different indoor environment,which further promotes the development of indoor positioning technology.