Parallel Irregular Fusion Estimation Based on Nonlinear Filter for Indoor RFID Tracking System

Abstract

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 K to [Formula: see text] 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.