Improving Accuracy and Robustness in HF-RFID-Based Indoor Positioning With Kalman Filtering and Tukey Smoothing

Abstract

In this article, we present a scalable, robust, and accurate indoor positioning system that uses a passive high-frequency radio frequency identification (HF RFID)-based positioning measurement system combined with Tukey smoother and a linear Kalman filter to locate mobile objects with an average measurement error of less than 3.7 cm. The proposed system is implemented and tested with extensive experiments, and our results show that the proposed system outperforms similar existing systems in minimizing the average positioning error and has better robustness against noisy sensor readings caused by hardware malfunctions or external error sources.