Deep Learning Based Situation Awareness for Multiple Missiles Evasion
Edvards Scukins Markus Klein Lars Kroon Petter Ögren
As the effective range of air-to-air missiles increases, it becomes harder
for human operators to maintain the situational awareness needed to keep a UAV
safe. In this work, we propose a decision support tool to help UAV operators in
Beyond Visual Range (BVR) air combat scenarios assess the risks of different
options and make decisions based on those. Earlier work focused on the threat
posed by a single missile, and in this work, we extend the ideas to several
missile threats. The proposed method uses Deep Neural Networks (DNN) to learn
from high-fidelity simulations to provide the operator with an outcome estimate
for a set of different strategies. Our results demonstrate that the proposed
system can manage multiple incoming missiles, evaluate a family of options, and
recommend the least risky course of action.