Research On Indoor Positioning Method Of RFID Based On RSSI

Location information is playing a more and more important role in the field of internet of things.Radio frequency identification(RFID)technology is the core one to support the internet of things,which identifies and manages target through the RF communication.It would have a good application prospect to research on obtaining the target location information based on RFID technology.At present,RFID indoor positioning mainly bases on the reader to read the tag signal strength(RSSI),however,the environmental noise and multipath effects will affect the positioning performance of RFID,so it is very important to study the RFID indoor positioning method based on RSSI.Firstly,the architecture and working principle of RFID are introduced,combined with the radar differential backscatter power of tag and Friis propagation equation,the link model of passive UHF RFID system under free space is deduced,and the model formula of practical application is given.The effects among the mutual coupling effect in the tag dense environment,the multipath effect under the reflection condition and the tag attachments on the RFID system link are analyzed in theory.Secondly,aiming to the small sample problem which used the reference tags in the RFID indoor positioning system,a RFID indoor positioning algorithm based on support vector regression(SVR)is proposed.Compared with the classical LANDMARC method,the performance of the positioning algorithm based on SVR is tested in MATLAB,and the influences of mutual coupling effect and multipath effect on the positioning results of the algorithm are also tested.The results show that the proposed method can improve the positioning accuracy by at least 25.2% compared with the LANDMARC method without increasing the number of reference tags.Finally,for the problems such as small-scale fading,tag attachments and other factors,which would led to low accuracy and poor robustness of passive UHF RFID positioning and tracking,a strong tracking Rauch-Tung-Striebel smoothing algorithm based on cubature kalman filter(STCKF-RTS)is proposed.On the basis of the traditional strong tracking filter theory,the third-degree spherical-radial cubature rule is used to replace the Jacobi matrix,which not only inherits the strong robustness of STF,but also improves the ability to deal with nonlinear system,and the RTS backward smoothing theory further improves the accuracy.In the opening indoor environment,with the three factors of small-scale fading,co-frequency interference and tag attachments,the performance of the algorithm is tested.The experimental results show that with the effects of these three factors,the accuracy of the proposed algorithm is improved at least 24.1%,7.1%,7.6% compared with other tracking algorithms.