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Indoor positioning and navigation have emerged as critical areas of research due to the limitations of GPS in enclosed environments. This study presents an innovative approach to high-precision indoor localization by employing the Extended Kalman Filter (EKF). Unlike traditional methods that often suffer from noise and multi-path effects, the EKF methodology accounts for nonlinearities and offers a recursive solution to estimate the state of dynamic systems. We deployed a sensor on a mobile robot that needs to move in an indoor environment while there is a moving obstacle that is moving around. Our findings demonstrate a significant accuracy in locating the obstacle while maneuvering inside the environment.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Mohammad AlShabi, S. Andrew Gadsden, Khaled Obaideen, Talal Bonny, "High-precision indoor localization using the extended Kalman filter approach," Proc. SPIE 13049, Laser Radar Technology and Applications XXIX, 130490O (5 June 2024); https://doi.org/10.1117/12.3015941