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.

引用

页数：9

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