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.

翻译：这项工作调查了使用深神经网络(DNN)对武器接触区(WEZ)最大发射范围进行估计的情况。WEZ允许试验者确定一个空域,在空域中,现有导弹有可能成功接触特定目标,即对手易遭射击的飞机周围的假设区域。我们建议采用一种方法,利用在变异条件下的5万模拟发射来确定特定导弹的WEZ。这些模拟用于培训DNN,该空域可在飞机发现不同发射条件时预测EEZ,确定系数为0.99。 该空域提供了另一个与先前研究有关的程序,因为它使用的是非分解型模型,即一次性考虑WEZ的所有方向,而以前没有这样做。此外,拟议方法使用一种实验设计,可以减少模拟运行,提供更快的示范培训。