Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network

Abstract

This work investigates the use of a Deep Neural Network (DNN) to perform an estimation of the Weapon Engagement Zone (WEZ) maximum launch range. The WEZ allows the pilot to identify an airspace in which the available missile has a more significant probability of successfully engaging a particular target, i. e., a hypothetical area surrounding an aircraft in which an adversary is vulnerable to a shot. We propose an approach to determine the WEZ of a given missile using 50,000 simulated launches in variate conditions. These simulations are used to train a DNN that can predict the WEZ when the aircraft finds itself on different firing conditions, with a coefficient of determination of 0.99. It provides another procedure concerning preceding research since it employs a nondiscretized model, i.e., it considers all directions of the WEZ at once, which has not been done previously. Additionally, the proposed method uses an experimental design that allows for fewer simulation runs, providing faster model training.

@inproceedings{dantas2021weapon,
  author = {Dantas, Joao P. A. and Costa, Andre N. and Geraldo, Diego and Maximo, Marcos R. O. A. and Yoneyama, Takashi},
  title = {{Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network}},
  booktitle = {Intelligent Systems: 10th Brazilian Conference, BRACIS 2021, Virtual Event, November 29 -- December 3, 2021, Proceedings, Part II},
  series = {Lecture Notes in Computer Science},
  year = {2021},
  pages = {193--207},
  numpages = {15},
  doi = {10.1007/978-3-030-91699-2_14},
  url = {https://doi.org/10.1007/978-3-030-91699-2_14},
  isbn = {978-3-030-91698-5},
  publisher = {Springer},
  address = {Cham, Switzerland},
  abstract = {This work investigates the use of a Deep Neural Network (DNN) to perform an estimation of the Weapon Engagement Zone (WEZ) maximum launch range. The WEZ allows the pilot to identify an airspace in which the available missile has a more significant probability of successfully engaging a particular target, i.e., a hypothetical area surrounding an aircraft in which an adversary is vulnerable to a shot. We propose an approach to determine the WEZ of a given missile using 50,000 simulated launches in variate conditions. These simulations are used to train a DNN that can predict the WEZ when the aircraft finds itself on different firing conditions, with a coefficient of determination of 0.99. It provides another procedure concerning preceding research since it employs a non-discretized model, i.e., it considers all directions of the WEZ at once, which has not been done previously. Additionally, the proposed method uses an experimental design that allows for fewer simulation runs, providing faster model training.},
  keywords = {Air combat, Deep neural network, Weapon engagement zone}
}