Iterative Bias Estimation for an Ultra-Wideband Localization System

Master thesis (2019)

Authors

Bas van der Heijden Mechanical Engineering

Contributors

Sergio Grammatico Team Bart De Schutter - Mechanical, Maritime and Materials Engineering (supervisor 1)
Jens Kober Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (supervisor 2)
Laura Ferranti Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (supervisor 2)
Manon Kok Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2019 Bas van der Heijden
Classification Sensor fusion Recursive least squares Ultra-wideband technology Adaptive observer design Bayesian methods Non line-of-sight
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Published Date

20-12-2019

Language

English

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Faculty

Mechanical Engineering

Abstract

Three bias estimation frameworks are presented that mitigate position-dependent ranging errors often present in ultra-wideband localization systems. State estimation and control are integrated, such that the positioning accuracy improves over iterations. The frameworks are experimentally evaluated on a quadcopter platform. Two state augmentation frameworks show that the anchor placement has a significant influence on the observability of the problem. A third framework circumvented any observability issues by using a classifier. This framework performed best as it improves the tracking performance with respect to ground truth, and also smoothens the overall flight by significantly reducing unwanted oscillations; see https://youtu.be/J-htfbzf40U for a video.