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http://dx.doi.org/10.12673/jant.2021.25.6.441

A Kalman filter with sensor fusion for indoor position estimation  

Janghoon Yang (Department of AI Software Engineering, Seoul Media Institute of Technology)
Publication Information
Journal of Advanced Navigation Technology / v.25, no.6, 2021 , pp. 441-449 More about this Journal
Abstract
With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.
Keywords
Bluetooth; IMU; Indoor positioning system; Kalman filter; Position estimation;
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