Identification method of student card chip based on Internet of things radio frequency identification

The Thesis introduces an accurate location method facing campus student identity recognition on the basis of ultrahigh frequency RFID fingerprint. First, we obtain the fingerprint information in the sensor installed on the robot during the training stage, introduce the particle filter location algorithm on the basis of recurrence Bayes filter during the location stage and analyze the influence on location accuracy by measurement algorithm with different similarity during the correction stage; then, propose a new similarity measurement algorithm, Hsim, and conduct weight calculation on the prediction model combined with an observation model established by k-neighbor; finally, accurately estimate the robot's actual pose using re-sampling algorithm. Experiments have proven the location system's accuracy and effectiveness; besides, a precision of around 0.21539m average location error is resulted. Experiment results show that the RFID fingerprint-based particle filter location system can preferably achieve overall location of mobile robot and that the system is presented with higher precision and robustness.