基于深度神经网络的武器交战区最大射程估计
Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network
摘要：
本文研究了利用深度神经网络（DNN）对武器交战区（WEZ）最大发射距离进行估计。WEZ允许飞行员识别可用导弹成功攻击特定目标的概率更大的空域，即，一个假想的飞机周围的区域，在那里敌人容易受到攻击。我们提出了一种方法来确定一个给定的导弹的WEZ使用50,000模拟发射在不同的条件下。这些模拟用于训练DNN，当飞机发现自己处于不同的点火条件时，DNN可以预测WEZ，确定系数为0.99。它提供了另一个程序有关先前的研究，因为它采用了非离散化模型，即，它同时考虑了WEZ的所有方向，这是以前没有做过的。此外，所提出的方法使用实验设计，允许更少的模拟运行，提供更快的模型训练。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
