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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Acknowledgement
This study is funded by both TUBITAK Grant No: 3001:215E156 and Ankara University BAP Grant no: 16B0443003.
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Unal, M., Bostanci, E., Guzel, M.S., Unal, F.Z., Kanwal, N. (2020). Evolutionary Motion Model Transitions for Tracking Unmanned Air Vehicles. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_120
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DOI: https://doi.org/10.1007/978-3-030-41862-5_120
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