期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2016
卷号：2016
DOI：10.1155/2016/1472930
出版社：Hindawi Publishing Corporation
摘要：In practical RFID tracking systems, usually it is impossible that the readers are placed right with a “grid” structure, so effective estimation method is required to obtain the accurate trajectory. Due to the data-driven mechanism, measurement of RFID system is sampled irregularly; therefore the traditional recursive estimation may fail from to sampling point. Moreover, because the distribution density of the readers is nonuniform and multiple measurements might be implemented simultaneously, fusion of estimations also needs to be considered. In this paper, an irregular estimation strategy with parallel structure was developed, where the dynamic model update and states fusion estimation were processed synchronously to achieve real-time indoor RFID tracking. Two nonlinear estimation methods were proposed based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF), respectively. The tracking performances were compared, and the simulation results show that the developed UKF method got lower covariance in indoor RFID tracking while the EKF one cost less calculating time.
