Paper Info
Title
EKF-based and Geometry-based Positioning under Location Uncertainty of Access Nodes in Indoor Environment
Abstract
High accuracy positioning enabled by 5G cellular networks will play a crucial role in the robot-based industrial applications, where the vertical accuracy will be as significant as the 3D accuracy. Aiming at target applications relying on flying robots in industrial environments, this paper presents and formulates two positioning algorithms when the location uncertainty of the access nodes (ANs) is taken into consideration. The first algorithm is a low-complexity geometry-based 3D positioning algorithm that utilizes both time-of-arrival and angle-of-arrival measurements. The second algorithm relies on extended Kalman Filter (EKF)-based positioning, by mapping the ANs' location uncertainty into the measurement noise statistics. The performance of the two proposed method is studied in terms of 3D and vertical positioning accuracy, sensitivity to location uncertainty of the ANs, and computational complexity in indoor scenarios. Based on the conducted complexity analysis, the proposed geometry-based algorithm is computationally more efficient than the EKF-based algorithm. In addition, the proposed geometry-based positioning method demonstrates a higher robustness against a high location uncertainty of ANs than the considered EKF-based method.
Year
DOI
Venue
2019
10.1109/IPIN.2019.8911785
2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
Keywords
Field
DocType
robot tracking,5G networks,indoor positioning,non-linear mapping,location uncertainty of access nodes
Computer vision,Extended Kalman filter,Real-time computing,Artificial intelligence,Engineering
Conference
ISSN
ISBN
Citations 
2162-7347
978-1-7281-1789-8
0
PageRank 
References 
Authors
0.34
5
5
Name
Order
Citations
PageRank
Yi Lu1812.85
Mike Koivisto200.34
Jukka Talvitie314117.35
Mikko Valkama41567175.51
ELENA SIMONA LOHAN532757.58