High-Precision Indoor Localization Using the Extended Kalman Filter Approach

被引：0
作者：
AlShabi, Mohammad [1 ]
Gadsden, S. Andrew [2 ]
Obaideen, Khaled [3 ]
Bonny, Talal [4 ]
机构：
[1] Univ Sharjah, Dept Mech & Nucl Engn, POB 27272, Sharjah, U Arab Emirates
[2] McMaster Univ, Dept Mech Engn, Hamilton, ON N1G 2W1, Canada
[3] Bio Sensing & Bio Sensors Grp, Smart Automat & Commun Technol, RISE, Sharjah, U Arab Emirates
[4] Univ Sharjah, Dept Comp Engn, POB 27272, Sharjah, U Arab Emirates
来源：
LASER RADAR TECHNOLOGY AND APPLICATIONS XXIX | 2024年 / 13049卷
关键词：
Extended Kalman Filter; estimation strategies; target tracking; mobile robot; LITHIUM-ION BATTERY; ARTIFICIAL NEURAL-NETWORKS; UNMANNED AERIAL VEHICLE; USEFUL LIFE PREDICTION; OF-CHARGE ESTIMATION; STATE ESTIMATION; PARAMETER; SYSTEMS; OBSERVER; DESIGN;
D O I：
10.1117/12.3015941
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
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.
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页数：9
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