Research On The Indoor Localization Of UHF RFID Based On Maching Learning

With the appearance of the new concepts of "Smart Planet",the Internet of Things(IoT)has received a large amount of attention from academia and industry.The key technology of radio frequency identification(RFID)has become a hot topic in recent years.In the past,RFID was mainly used in personnel management and logistics,which was rarely used in indoor localization.UHF RFID has the advantages of low cost,fast reading speed and long distance in the metropolitan area,which can meet the requirements of indoor localization better.It is vital to obtain the location of objects and people anytime.The research of UHF RFID indoor localization based on machine learning is studied and the accurate indoor localization of RFID tags is realized.The specific research contents are as follows:(1)An algorithm of the combination of kNN and Bayesian estimation is proposed.The experiments are made in an area of 2m×2m,which show that the accuracy of the positioning is significantly improved compared with single k NN or Bayesian algorithm.At the same time,the Gauss filtering is added to improve the anti-jamming ability of the positioning system.(2)The phase of the RFID tag is important information for achieving high-precision indoor localization.In order to obtain the phase information of the RFID tag,a software is designed based on the SDK.The PC software can obtain the identification of the tag,the setting of the parameters and the storage of the information,which can basically meet the requirements of the project.(3)The phase of RFID tags and the phase difference between the two antennas are studied.Combined with theoretical knowledge,the distribution of phase differences in two-dimensional space is obtained through simulation experiments.Then,based on the simulation experiment,three different antenna placement structures are designed.The effects of the antenna placement structures on the indoor positioning accuracy are analyzed.The most suitable antenna structure is selected to improve the accuracy of indoor localization.(4)An indoor localization algorithm based on BP-SVR is proposed.The hidden layer of BP neural network is used to enhancing data dimension of the signal strength and phase difference,and then input into the SVR model for regression.The experiment was done in the area of 6m×8m,which shows that this method can effectively improve the accuracy of indoor localization.