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Evolutionary Motion Model Transitions for Tracking Unmanned Air Vehicles

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Metehan Unal & Erkan Bostanci & Mehmet Serdar Guzel & Fatima Zehra Unal & Nadia Kanwal, 2020. "Evolutionary Motion Model Transitions for Tracking Unmanned Air Vehicles," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1193-1200, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_120
    DOI: 10.1007/978-3-030-41862-5_120
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