Research On Device-free Localization And Tracking Technology Based On Intersection Multidimensional Feature For UHF RFID

With the rapid development of smart world technology,the demands of obtaining target information have become urgent requirements.Location-Based Services(LBSs)have become a key technology for the realization of a smart world,and have attracted much attention in recent years.The Device-Free Localization(DFL)can locate the target without requiring the target to carry any radio equipment,which has a broad application prospect in the field of indoor positioning.Radio Frequency Identification(RFID),as a key technology for Io T implementation,has been widely used in objects identification and tracking.RFID-based Radio Tomographic Imaging(RTI)method has become a hot spot in the field of localization research due to its low cost,excellent realtime performance and easy to deployment.However,in a complex indoor environment,affected by factors such as non-line-ofsight propagation and noise effects,the Received Signal Strength(RSS)will change unpredictably.The RTI imaging results will produce false targets,which leads to a decrease in localization accuracy.Accordingly,in the indoor environment,how to effectively identify the location of the target is still a challenging problem.In addition,the device-free tracking problem caused by the dynamic effect brought by the target movement is also the focus of this thesis.In this thesis,a device-free localization method based on Weighted Intersection Multidimensional Feature(WMIF-RTI)is proposed to solve the problem that the accuracy of multi-target device-free localization decreases.The target impact region is introduced to extract the effective intersections,and then the multidimensional feature information is analyzed to determine the region where the real target may exist.Extensive experiments are conducted with results are demonstrating that the method achieves high localization accuracy in different indoor scenarios,while maintaining low computational complexity.The localization error can be limited to 1m with the probability of 65% for two-target localization,and the three-target localization error can be limited to 1m with a probability of 46%.Aiming at the problem that the localization method cannot track the target in real time during the movement of the target,this thesis proposes a fusion filter tracking algorithm based on the localization algorithm.By analyzing the target dynamic model,the state transition equations of target motion and RSS measurement are established.The image tracking method is used to track image pixels in real time,which ensures the accuracy of the observation value.The Kalman filter method is used to track the motion state of the target,which realizes the real-time calibration of the target position,and finally achieves the purpose of device-free tracking of dynamic targets.A large number of real experiments show that the fusion filtering algorithm can effectively track dynamic targets and obtain accurate motion trajectories.In the complex warehouse scenario,when the target moves at 10cm/s,the average tracking error can reach 0.3123 m,and the median error is 0.3247m.