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

arXiv: https://arxiv.org/abs/2111.04474 · HTML: https://mlsanity.com/paper/2111.04474/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Recurrent Neural Networks for Time Series Forecasting](https://mlsanity.com/paper/1901.00069/) (2019)

## Built on by (most cited first, 2 of 2)

- [Supervised Machine Learning for Effective Missile Launch Based on Beyond Visual Range Air Combat Simulations](https://mlsanity.com/paper/2207.04188/) (2022)
- [Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2304.09669/) (2023)

## Abstract (arXiv)

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.