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Simulation-Based Early Prediction of Rocket, Artillery, and Mortar Trajectories and Real-Time Optimization for Counter-RAM Systems

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  • Arash Ramezani
  • Hendrik Rothe

Abstract

The threat imposed by terrorist attacks is a major hazard for military installations, for example, in Iraq and Afghanistan. The large amounts of rockets, artillery projectiles, and mortar grenades (RAM) that are available pose serious threats to military forces. An important task for international research and development is to protect military installations and implement an accurate early warning system against RAM threats on conventional computer systems in out-of-area field camps. This work presents a method for determining the trajectory, caliber, and type of a projectile based on the estimation of the ballistic coefficient. A simulation-based optimization process is presented that enables iterative adjustment of predicted trajectories in real time. Analytical and numerical methods are used to reduce computing time for out-of-area missions and low-end computer systems. A GUI is programmed to present the results. It allows for comparison between predicted and actual trajectories. Finally, different aspects and restrictions for measuring the quality of the results are discussed.

Suggested Citation

  • Arash Ramezani & Hendrik Rothe, 2017. "Simulation-Based Early Prediction of Rocket, Artillery, and Mortar Trajectories and Real-Time Optimization for Counter-RAM Systems," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-8, August.
  • Handle: RePEc:hin:jnlmpe:8157319
    DOI: 10.1155/2017/8157319
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    Cited by:

    1. Zilong Zhuang & Liangxun Guo & Zizhao Huang & Yanning Sun & Wei Qin & Zhao-Hui Sun, 2021. "DyS-IENN: a novel multiclass imbalanced learning method for early warning of tardiness in rocket final assembly process," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2197-2207, December.

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