Evolutionary Motion Model Transitions for Tracking Unmanned Air Vehicles

Metehan Unal (), Erkan Bostanci (), Mehmet Serdar Guzel (), Fatima Zehra Unal () and Nadia Kanwal ()
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Metehan Unal: Ankara University, Computer Engineering Department
Erkan Bostanci: Ankara University, Computer Engineering Department
Mehmet Serdar Guzel: Ankara University, Computer Engineering Department
Fatima Zehra Unal: Ankara University, Computer Engineering Department
Nadia Kanwal: Lahore Collage for Women University

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1193-1200 from Springer

Abstract: 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.

Keywords: Unmanned air vehicles; Evolutionary algorithms; Tracking (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_120

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DOI: 10.1007/978-3-030-41862-5_120

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