Leveraging the Extended Kalman Filter to Minimize Infrastructure in THz RFID Systems for Indoor Sub-Millimeter Tracking Accuracy

This paper investigates the effectiveness of the extended Kalman filter (EKF) as a tracking algorithm for indoor radio frequency identification (RFID) systems operating at terahertz (THz) band, with a critical objective of achieving sub-millimeter (sub-mm) accuracy and minimizing the infrastructure dependency. We acknowledge the inherent trade-off between the number of anchors employed and the achievable accuracy. This study presents a comparative analysis of localizing and tracking a moving object between the least squares (LS) and the EKF estimators, focusing on the minimum number of anchors required to attain sub-mm accuracy.