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A Data-driven Method for Estimating Formation Flexibility in Beyond-Visual-Range Air Combat
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. Aeronaut Solut Div, SAAB Aeronaut, Stockholm, Sweden.ORCID iD: 0000-0003-4662-441X
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. Inst Adv Studies IEAv, Decis Support Syst Subdivis, Sao Jose Dos Campos, Brazil.ORCID iD: 0000-0001-9768-2340
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-7714-928X
2024 (English)In: 2024 International Conference on Unmanned Aircraft Systems (ICUAS), Institute of Electrical and Electronics Engineers (IEEE) , 2024, p. 241-247Conference paper, Published paper (Refereed)
Abstract [en]

Tactical decisions in air combat are typically evaluated using experience as a basis. Pilots undergo frequent training in various air combat processes to enhance their combat proficiency and evaluation skills. Having the Situational Awareness (SA) necessary to evaluate the effects of multiple missile threats can often be challenging. This study provides a new method for calculating an aircraft fleet's maneuver flexibility in a Beyond-Visual-Range (BVR) setting. Sustaining a high degree of flexibility is necessary to adapt to unforeseen circumstances in BVR air combat. To do that, we employ Deep Neural Networks (DNN) to capture the result of a highperformance aircraft model in the presence of adversarial BVR missiles. We then modify our approach to calculate the aircraft's maneuverability concerning an opposing fleet, looking at the advantages and disadvantages of several flight formations. Finally, we consider the anticipated threat from an incoming opponent formation and optimize the counter-formation. This methodology offers a more sophisticated comprehension of aircraft maneuver flexibility within a BVR framework and aids in developing flexible and efficient decision-making techniques for air combat.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024. p. 241-247
Series
International Conference on Unmanned Aircraft Systems, ISSN 2373-6720
Keywords [en]
Machine Learning, Beyond Visual Range Air Combat, Unmanned Aerial Vehicle, Situation Awareness
National Category
Robotics and automation
Identifiers
URN: urn:nbn:se:kth:diva-352726DOI: 10.1109/ICUAS60882.2024.10557091ISI: 001259354800175Scopus ID: 2-s2.0-85197420643OAI: oai:DiVA.org:kth-352726DiVA, id: diva2:1895353
Conference
International Conference on Unmanned Aircraft Systems (ICUAS), JUN 04-07, 2024, Chania-Crete, GREECE
Note

Part of ISBN 979-8-3503-5789-9, 979-8-3503-5788-2

QC 20240905

Available from: 2024-09-05 Created: 2024-09-05 Last updated: 2025-03-06Bibliographically approved
In thesis
1. Data-Driven Methods for Enhanced Situation Awareness in Beyond Visual Range Air Combat
Open this publication in new window or tab >>Data-Driven Methods for Enhanced Situation Awareness in Beyond Visual Range Air Combat
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Pilots must be aware of their surroundings and environment to outperform the enemy fleet in air combat. Situation Awareness (SA) is vital. Pilots with a superior SA, compared to that of the enemy are more likely to act correctly and more quickly, which in turn increases their chances of outperforming the enemy fleet. For this reason, SA plays a significant role in the battlefield, and the techniques that provide pilots with SA must evolve with the ever-changing battlefield as air-to-air missiles' effective range increases and their performance improves.

We introduce our work in the SA domain for Beyond Visual Range (BVR) air combat. First, we describe the environment in which BVR air combat unfolds, followed by the research challenges where we address developing machine learning-driven tactics for BVR combat to optimize engagement strategies in complex and uncertain environments. Finally, we present our research results and explain how our approach can be applied to engagements with an arbitrary number of enemies and friendly units while noting that our approach should benefit both manned and unmanned aerial vehicles.

Abstract [sv]

För stridspiloter är det väldigt viktigt att vara medvetna om sin omgivning, vad gäller position och status hos både fiender och egna styrkor. Denna situationsmedvetenhet(eng. Situation Awareness, SA) är avgörande för att piloterna skall kunna agera snabbt och korrekt, och därmed vinna striden.SA är således mycket viktigt, och metoder som förbättrar SA utvecklas därför ständigt, parallellt med övrig utveckling av både materiel och taktik.

I denna avhandling presenteras vårt arbete inom SA för luftstrider där fienden befinner sig på långa avstånd (Beyond Visual Range, BVR). Först beskrivs  forskningsutmaningar med speciellt fokus på maskininlärningsdriven taktik för BVR-strider. Sedan presenterar vi våra forskningsresultat och förklarar hur de kan tillämpas i situationer med både bemannade och obemannade flygfarkoster, och med olika antal enheter på respektive sida.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2025. p. 66
Series
TRITA-EECS-AVL ; 2025:27
Keywords
Cooperative control, Optimization, Control Barrier Functions, Reinforcement Learning, Machine Learning, Beyond Visual Range Air Combat, Situation Awareness
National Category
Computer and Information Sciences
Research subject
Aerospace Engineering; Computer Science
Identifiers
urn:nbn:se:kth:diva-360931 (URN)978-91-8106-213-7 (ISBN)
Public defence
2025-03-28, Kollegiesalen, Brinellvägen 6, Stockholm, 14:00 (English)
Opponent
Supervisors
Funder
Vinnova, 2017-04875
Note

QC 20250306

Available from: 2025-03-06 Created: 2025-03-06 Last updated: 2025-10-30Bibliographically approved

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Scukins, EdvardsCosta, Andre NegrãoÖgren, Petter

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