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Accuracy Improvement of Indoor Real-Time Location Tracking Algorithm for Smart Supermarket Based on Ultra-Wideband

Authors :
Hu Guohua
Wang Shengjie
Huimin Duan
Yuwu Feng
Juanjuan Gu
Juan Zheng
Pascal Feldhaus
Source :
International Journal of Pattern Recognition and Artificial Intelligence. 33:2058004
Publication Year :
2019
Publisher :
World Scientific Pub Co Pte Lt, 2019.

Abstract

Collecting data like location information is an essential part of concepts like the “IoT” or “Industry 4.0”. In the case of the development of a precise localization system and an integrated navigation system, indoor location technology receives more and more attention and has become a hot research topic. Common indoor location techniques are mainly based on wireless local area network, radio frequency tag, ZigBee technology, Bluetooth technology, infrared technology and ultra-wideband (UWB). However, these techniques are vulnerable to various noise signals and indoor environments, and also the positioning accuracy is easily affected by the complicated indoor environment. We studied the problem of real-time location tracking based on UWB in an indoor environment in this paper. We have proposed a combinational filtering algorithm and an improved Two-Way Ranging (ITWR) method for indoor real-time location tracking. The simulation results prove that the real-time performance and high accuracy of the presented algorithm can improve location accuracy. The experiment shows that the combinational algorithm and ITWR method which are applied to the positioning and navigation of the smart supermarket, have achieved quiet good results in positioning accuracy. The average positioning error is less than 10[Formula: see text]cm, some of the improvements can elevate the positioning accuracy by 17.5%. UWB is a suitable method for indoor real-time location tracking and has important theoretic value and practical significance.

Details

ISSN :
17936381 and 02180014
Volume :
33
Database :
OpenAIRE
Journal :
International Journal of Pattern Recognition and Artificial Intelligence
Accession number :
edsair.doi...........a703057766b851ebd36894d36e70dce5