TOF Indoor Location Algorithm based on RBF Neural Network

Hongmei Zhao, Jielei Zhao

2019

Abstract

In the UWB positioning system, due to the existence of multipath effects, NLOS and other factors, a certain degree of measurement error will result. In particular, the NLOS error has become a key factor affecting the positioning accuracy. Large NLOS errors often lead to a sharp decline in the positioning performance of UWB Indoor Positioning System. In this paper, a large number of data with NLOS error is used as a sample, and it is trained by RBF artificial neural network algorithm. In this way, the influence of the NLOS error can be eliminated at the source, and the positioning accuracy of the TOF can be improved.

Paper Citation

in Harvard Style

Zhao H. and Zhao J. (2019). TOF Indoor Location Algorithm based on RBF Neural Network.In Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC, ISBN 978-989-758-357-5, pages 128-134. DOI: 10.5220/0008097901280134

in Bibtex Style

@conference{ctisc19,
author={Hongmei Zhao and Jielei Zhao},
title={TOF Indoor Location Algorithm based on RBF Neural Network},
booktitle={Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC,},
year={2019},
pages={128-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008097901280134},
isbn={978-989-758-357-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC,
TI - TOF Indoor Location Algorithm based on RBF Neural Network
SN - 978-989-758-357-5
AU - Zhao H.
AU - Zhao J.
PY - 2019
SP - 128
EP - 134
DO - 10.5220/0008097901280134