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An End-to-End LSTM-MDN Network for Projectile Trajectory Prediction

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning (IScIDE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11936))

Abstract

Trajectory prediction from radar data is an example of a signal processing problem, which is challenging due to small sample sizes, high noise, non-stationarity, and non-linear. A data-driven LSTM-MDN end-to-end network from incomplete and noisy radar measurements to predict projectile trajectory is investigated in this paper. Traditional prediction algorithm usually uses Kalman Filter (KF) or the like to estimate target’s position and speed, then uses the numerical integral, such as Runge-Kutta, Adams, etc. to extrapolate the launch point or impact point, which mainly relies on the accuracy of dynamic models. A Long Short-Term Memory (LSTM) network is designed to estimate the real position from sampled and noisy radar measurements series, and a Mixture Density Network (MDN) is developed for trajectory extrapolation and projectile launch point prediction. These two subnetworks are integrated into an end-to-end network, which is trained by the radar measurement samples of a projectile and the corresponding ground truth of its launch point. Compared with the traditional methods, amount of experiments show that our proposed method is superior to the traditional model-based methods, and its adaptability to the range of initial launch angle is significantly better than the traditional method.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Li-he Hou & Hua-jun Liu

Authors
  1. Li-he Hou
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  2. Hua-jun Liu
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Corresponding author

Correspondence to Hua-jun Liu .

Editor information

Editors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Zhen Cui

  2. Nanjing University of Science and Technology, Nanjing, China

    Jinshan Pan

  3. Nanjing University of Science and Technology, Nanjing, China

    Shanshan Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Liang Xiao

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Hou, Lh., Liu, Hj. (2019). An End-to-End LSTM-MDN Network for Projectile Trajectory Prediction. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-36204-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36203-4

  • Online ISBN: 978-3-030-36204-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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