Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Objective Evaluation of Recurrent Neural Network Based Techniques for Trajectory Prediction of Flight Vehicles

비행체의 궤적 예측을 위한 순환 신경망 기반 기법들의 정량적 비교 평가에 관한 연구

  • 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)
  • Received : 2021.08.19
  • Accepted : 2021.09.15
  • Published : 2021.09.30
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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.

본 논문에서는 비행체의 궤적 예측에 적절한 순환 신경망 기반 기법을 탐구하기 위하여 정량적인 비교 평가 연구를 수행하였다. 이를 위해 본 논문에서는 비행체의 궤적 예측 작업을 수행하기 위한 다양한 입력 및 출력 관계들을 정의하고 동일한 실험 환경에서 비교 평가하였다. 특히 비행체 위치의 상대값 기반 입출력 관계를 제안하여 비행체의 궤적 예측에 적합함을 보였다. 또한 비행체 궤적 예측에 효율적인 네트워크 구조 및 하이퍼파라미터를 결정하기 위한 다양한 ablation study(비교 실험)를 진행하였다. 본 논문에서 제시된 정량적 비교 평가 결과는 비행체의 궤적 예측을 위해 순환 신경망 기반 기법을 이용하거나 비행체의 궤적 예측에 특화된 딥러닝 기법을 연구하고자 하는 연구자 및 개발자들에게 실질적인 도움이 될 것으로 예상된다.

Keywords

References

  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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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.
  7. A. Graves, S. Fernandez, and J. Schmidhuber, "Multi-dimensional recurrent neural networks," International conference on artificial neural networks, pp.549-558, 2007.
  8. 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
  9. 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
  10. 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
  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.