Improving UWB Based Indoor Positioning in Industrial Environments through Machine Learning

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Improving UWB Based Indoor Positioning in Industrial Environments through Machine Learning
Title:
Improving UWB Based Indoor Positioning in Industrial Environments through Machine Learning
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2018 International Conference on Control, Automation, Robotics and Vision
Publication Date:
07 May 2021
Citation:
Abstract:
The detection and mitigation of Non-Line-of- Sight (NLOS) signals are crucial for achieving the full potential of UWB-based indoor positioning. In dense multipath industrial environments, it was seen that using the power characteristics of the received signal to identify NLOS conditions is effective when tracking stationary objects but is insufficient for mobile object tracking. Hence, machine learning classifiers utilizing Multi-Layer Perceptron (MLP) and Boosted Decision Trees (BDT) were developed to improve NLOS detection. Through experimental results from tests in a factory scenario, it is shown that BDT yields a higher accuracy of 87% as compared to the 79% obtained by the received power-based method.
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Funding Info:
This work has been carried with funding provided under the Model Factory @ ARTC program; IAF-PP PG/20170607/005, CA/20180214/045
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© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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