Research On Positioning Algorithm Of RFID Tag Based On RSSI

With the rapid development of Internet of things technology,outdoor positioning technology has been unable to meet people's needs and indoor positioning technology has attracted a lot of attention.Radio frequency identification(RFID)is widely concerned with its advantages of low cost,non-line-of-sight and non-contact for indoor positioning researches.The algorithms proposed in this thesis are all RFID tag positioning based on Received Signal Strength Indication(RSSI).In the scene where the target tag is still,this thesis studies and improves the classical RFID indoor positioning algorithm and proposes the positioning algorithm based on support vector regression and particle swarm optimization(SVR-PSO),which effectively improves the positioning accuracy of indoor positioning.In the scene of the target tag movement,this thesis studies the indoor positioning algorithm based on unscented kalman filter and rauch tung striebel smoother(UKF-RTS),which effectively reduces the positioning error.The main contributions of this thesis are as follows.(1)This thesis briefly introduces the composition and working principle of the RFID system,and gives some common models of signal propagation.In addition,the principle of the commonly used indoor positioning method is described and its advantages and disadvantages are analyzed.(2)The RFID positioning algorithm based on reference tag is improved.The localization algorithm of location identification based on dynamic active RFID calibration(LANDMARC)system and active RFID based localization using virtual reference elimination(VIRE)system are studied and simulated.Aiming at improving the shortcomings of the VIRE algorithm,the improved VIRE algorithm is proposed by the gravity Lagrange interpolation method,the boundary virtual reference label and the adaptive threshold.The improved VIRE algorithm is simulated by Python.The simulation results show that compared with the classic VIRE algorithm,the improved VIRE algorithm effectively improves the positioning accuracy of the label in the boundary region.(3)An indoor location algorithm SVR-PSO is proposed.The algorithm uses support vector regression(SVR)to construct the nonlinear mapping relation between RSSI and distance between the tags and the readers.In addition,through applying particle swarm optimization(PSO)to optimize the objective function,the coordinate position of target tag can be estimated.The experimental results show that SVR-PSO algorithm can achieve high positioning accuracy in both the central region and the boundary region.(4)Based on the SVR-PSO algorithm,an indoor positioning algorithm UKF-RTS is proposed.The algorithm is based on unscented kalman filter(UKF)theory and incorporates SVR-PSO algorithm and piecewise rauch tung striebel(RTS)smoothing theory.It has the characteristics of high positioning accuracy and algorithm stability.The simulation results show that the UKFRTS algorithm effectively reduces the positioning error during trajectory tracking,and the positioning error is relatively small when the target motion state changes.The positioning performance is relatively stable.