Rfid Reader Positioning Algorithm

With the rapid development of wireless location technology and wireless sensor networks and the Internet of things, a single positioning system will be difficult to meet the needs of the user's orientation. Multi-location system integrated into the development trend of the future positioning application. GPS global positioning system has been using large-scale commercial. However, RFID-based positioning technology has become the star position of concern recently.In the thesis, we reviewed the current radio frequency identification (Radio Frequency Identification, RFID) location technology in terms with the basic positioning methods, firstly. After that, we studied the positioning technology about RFID reader with respect to the indoor wireless communication environment. The study focused on the following:On the one hand, under the Gaussian noise model, we accomplish reader positioning by using Bayesian estimation methods according to the indoor experience loss model. Especially, focuses on two types of Bayesian methods, namely, (Extended Kalman filter (EKF) algorithm, Unscented Kalman Filter (UKF) algorithm) in low SNR on reader positioning performance, as well as under different environmental characteristics and analysis both algorithms'respond to the positioning performance of the RFID reader. As a result of the Complexity in indoor environment, we studied the positioning technology based on the database which successfully overcome multi-path effects and non line of sight (NLOS) in respond to measurement error and eventually lead to decline in positioning accuracy by using the similarity principle of space. Meanwhile, According to this method's shortcoming, we proposed a kind of new positioning technology named the database interpolation techniques, this estimator is under condition that the minimum mean square error principle, which is also a best linear unbiased estimator (BLUE). Simulation illustrates the proposed positioning algorithm improved the positioning performance and confirmed the improved algorithm effectiveness.Finally, we give a summary of the thesis and pointed out the problems need to solve as well as further study on the issue.