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http://dx.doi.org/10.7471/ikeee.2021.25.3.540

Objective Evaluation of Recurrent Neural Network Based Techniques for Trajectory Prediction of Flight Vehicles  

Lee, Chang Jin (The 5th R&D Institute-1st Directorate, Agency for Defense Development)
Park, In Hee (The 5th R&D Institute-1st Directorate, Agency for Defense Development)
Jung, Chanho (Dept. of Electrical Engineering, Hanbat National University)
Publication Information
Journal of IKEEE / v.25, no.3, 2021 , pp. 540-543 More about this Journal
Abstract
In this paper, we present an experimental comparative study of recurrent neural network based techniques for trajectory prediction of flight vehicles. We defined and investigated various relationships between input and output under the same experimental setup. In particular, we proposed a relationship based on the relative positions of flight vehicles. Furthermore, we conducted an ablation study on the network architectures and hyperparameters. We believe that this comprehensive comparative study serves as a reference point and guide for developers in choosing an appropriate recurrent neural network based techniques for building (flight) vehicle trajectory prediction systems.
Keywords
Flight vehicles; Trajectory prediction; Recurrent neural network; Long short-term memory network; Objective evaluation;
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1 T. Jin, "Tracking of Person Walking Pattern and Trajectory Following with 2D Laser Scanner," The Transactions of the Korean Institute of Electrical Engineers, vol.67, no.7, pp.903-909, 2018. DOI: 10.5370/KIEE.2018.67.7.903   DOI
2 M. Gao, G. Shi, and S. Li, "Online prediction of ship behavior with automatic identification system sensor data using bidirectional long short-term memory recurrent neural network," Sensors, vol.18, no.12, pp.4211-4226, 2018. DOI: 10.3390/s18124211   DOI
3 S. Jang, "The Optical Tracking Method of Flight Target using Kalman Filter with DTW," J. Adv. Navig. Technol., vol.25, no.3, pp.217-222, 2021. DOI: 10.12673/jant.2021.25.3.217   DOI
4 Z. Shi, M. Xu, Q. Pan, B. Yan and H. Zhang, "LSTM-based Flight Trajectory Prediction," 2018 International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2018. doi: 10.1109/IJCNN.2018.8489734.   DOI
5 J. Kim, C. Jung, D. Kang, and C. J. Lee, "A New Vessel Path Prediction Method using Long Short-term Memory," The Transactions of the Korean Institute of Electrical Engineers, vol.69, no.7, pp. 1131-1134, 2020. DOI: 10.5370/KIEE.2020.69.7.1131   DOI
6 S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol.9, no.8, pp.1735-1780, 1997. DOI: 10.1162/neco.1997.9.8.1735   DOI
7 S. Choi, J. Kim, H. Yu, D. Ka, and H. Yeo, "Deep-learning Based Urban Vehicle Trajectory Prediction," Journal of Korean Society of Transportation, vol.37, no.5, pp.422-429, 2019. DOI: 10.7470/jkst.2019.37.5.422   DOI
8 J. H. Oh, S. H. Lee, B. H. Lee, and J.-I. Park, "Statistical Model of 3D Positions in Tracking Fast Objects Using IR Stereo Camera," Journal of The Institute of Electronics and Information Engineers, vol.52, no.1, 2015. http://dx.doi.org/10.5573/ieie.2015.52.1.089   DOI
9 A. Graves, S. Fernandez, and J. Schmidhuber, "Multi-dimensional recurrent neural networks," International conference on artificial neural networks, pp.549-558, 2007.
10 J. Kim, C. Jung, D. Kang, and C. J. Lee, "A New Vessel Path Prediction Method Based on Anticipation of Acceleration of Vessel," Journal of Institute of Korean Electrical and Electronics Engineers, vol.24, no.4, pp.1176-1179, 2020. DOI: 10.7471/ikeee.2020.24.4.1176   DOI
11 K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, "Learning phrase representations using RNN encoderd-ecoder for statistical machine translation," arXiv preprint arXiv:1406.1078, 2014.