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2020 | OriginalPaper | Chapter

Evolutionary Motion Model Transitions for Tracking Unmanned Air Vehicles

Authors : Metehan Unal, Erkan Bostanci, Mehmet Serdar Guzel, Fatima Zehra Unal, Nadia Kanwal

Published in: New Trends in Computational Vision and Bio-inspired Computing

Publisher: Springer International Publishing

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Abstract

Finding and tracking the position of an Unmanned Air Vehicles (UAV) is an important research problem since they are increasingly being used. These devices are equipped with GPS and orientation sensors which are used for tracking. However, data from these sensors can be missing or inaccurate in case of signal outages or other calibration problems. In this paper, we present evolutionary optimization of a rule-base designed for predicting motion models for a Kalman filter that is used to track the position and orientation of a UAV. Results show improved performance in terms of filter accuracy.

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Metadata
Title
Evolutionary Motion Model Transitions for Tracking Unmanned Air Vehicles
Authors
Metehan Unal
Erkan Bostanci
Mehmet Serdar Guzel
Fatima Zehra Unal
Nadia Kanwal
Copyright Year
2020
Publisher
Springer International Publishing
Book
New Trends in Computational Vision and Bio-inspired Computing

Print ISBN: 978-3-030-41861-8

Electronic ISBN: 978-3-030-41862-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-41862-5_120

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