TY - GEN
T1 - Nonlinear Kalman Filter Based Shop Floor RFID Data Fusion Algorithm
AU - Yuan, Kun
AU - Zhuang, Cunbo
AU - Liu, Jinshan
AU - Feng, Jindan
AU - Xiong, Hui
AU - Shi, Jiancheng
N1 - Publisher Copyright:
© 2022 Association for Computing Machinery.
PY - 2022/10/21
Y1 - 2022/10/21
N2 - 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.
AB - 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.
KW - RFID
KW - data fusion
KW - indoor positioning
KW - nonlinear Kalman filter
UR - http://www.scopus.com/inward/record.url?scp=85144284339&partnerID=8YFLogxK
U2 - 10.1145/3565387.3565440
DO - 10.1145/3565387.3565440
M3 - Conference contribution
AN - SCOPUS:85144284339
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 6th International Conference on Computer Science and Application Engineering, CSAE 2022
A2 - Emrouznejad, Ali
PB - Association for Computing Machinery
T2 - 6th International Conference on Computer Science and Application Engineering, CSAE 2022
Y2 - 21 October 2022 through 22 October 2022
ER -