The Research Of Moving Target Localization Method In UHF-RFIDs

UHF-RFID(Ultra High Frequency,Radio Frequency Identification)system has various advantages,such as non-contact,long identification distance,fast communication speed,easy deployment and low cost label.These advantages have made the UHF-RFID a positioning technology.Since the UHF-RFID radio frequency signal measurement information has non-neglectable errors caused by the influence of the factors such as environment noise and multipath propagation,which eventually affect the positioning precision of the UHF-RFIDs.With the increasing demand for wireless location technology,and the accuracy of the commonly used UHF-RFID localization method cannot meet practical demands,the study of UHF-RFIDs localization method has a great practical significance.In this thesis,we analyzed the characteristics of two commonly used UHF-RFID localization methods and its weakness.To improve the positioning accuracy of the UHF-RFIDs,the information of the UHF-RFIDs and the internal sensor information such as odometer,gyroscope of the moving target are fused.On the one hand,in view of the linear target kinematics model,a localization method is presented based on the linear Kalman filter bank.On the other hand,consider the nonlinear target kinematics model,and apply the filter bank to the nonlinear system,a localization method is proposed based on the adaptive UKF(Unscented Kalman Filter)bank.The experiments show that the adaptive UKF bank reduces the positioning error of the filter effectively.The main work of the thesis are summarized as follows:Firstly,the problem of the moving target localization with an unknown initial pose in UHF-RFIDs is investigated,and a localization method of linear Kalman filter bank is proposed.Due to the RSSI(Received Signal Strength Indication)ranging method of UHF-RFIDs often involves the problem of nonlinear measurement information,to avoid the negative impact caused by the nonlinearity of the measurement,the method of VIRE(Active RFID-based Localization Using Virtual Reference Elimination)is introduced to convert the nonlinear measurement into linear measurement.Meanwhile,consider that the heading angle of the moving target has large estimation error,by applying the filter bank method,the estimation of uncertainty of moving target heading angle is reduced effectively.Secondly,the filter bank is applied to nonlinear system,and a localization method of the moving target is presented based on the adaptive UKF bank.Although the method of the VIRE converts the nonlinear measurement into a linear one,and the linearization error is avoided,the nonlinear transformation has destroyed the gaussian property of the measurement information,which induce the quantization error.To compensate the uncertainty caused by quantization error,a hypothesis testing method is adopted,and an adaptive factor is introduced to the UHF-RFIDs.Finally,an experiment platform of the indoor mobile robot localization in UHF-RFIDs is designed and the validity of the proposed linear Kalman filter bank and the adaptive UKF bank is verified.The experiments show that the linear Kalman filter bank has improved the localization accuracy of the mobile robot.By adopting the method of adaptive UKF bank,the UHF-RFIDs positioning accuracy and robustness are improved as compared with the linear Kalman filter bank.