Deep Learning Based Situation Awareness for Multiple Missiles Evasion

• URL: http://arxiv.org/abs/2402.10101v1
• Date: Wed, 7 Feb 2024 14:21:21 GMT
• Title: Deep Learning Based Situation Awareness for Multiple Missiles Evasion
• Authors: Edvards Scukins, Markus Klein, Lars Kroon, Petter \"Ogren
• Abstract summary: 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. 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.
• Score: 1.7819574476785418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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