Nonlinear Kalman Filter Based Shop Floor RFID Data Fusion Algorithm

Abstract

Radio frequency identification (RFID) technology is one of the main means to obtain the location data of production elements such as personnel and materials in intelligent workshops, but its positioning accuracy has many uncertainties. In order to map the transportation trajectory of workshop materials in digital space more accurately, this paper adopts a nonlinear Kalman filter-based RFID data fusion algorithm. Firstly, the good estimation performance of nonlinear filters such as extended Kalman filter (EKF) and unscented Kalman filter (UKF) is utilized, and the motion process is determined by combining with the target dynamics model thus forming the fusion algorithm, and finally the data from multiple RFID readers are fused for path estimation and the final approximate trajectory is obtained. In the simulation experiments, after repeated experiments and comparison experiments with particle filter (PF) and Gauss-Hermite Kalman filter (GHKF) algorithms, it is found that the UKF-based fusion algorithm proves to have higher accuracy, and the EKF-based fusion algorithm has less computing time. In addition, the fusion performance of both methods is excellent in RFID readers sufficiency areas.