Missile Evasion Maneuver Generation with Model-free Deep Reinforcement Learning

Muhammed Murat Ozbek, Emre Koyuncu

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

2 Atıf (Scopus)

Özet

Unmanned Combat Aerial Vehicles (UCAVs) play a significant role in modern military conflicts as they can perform intelligence, surveillance, reconnaissance, and target acquisition missions while carrying aircraft ordnance like missiles and bombs, and Anti-Tank Guided Missiles (ATGMs). However, the increased use of UCAVs has also led to more advanced anti-UCAV solutions in air defense. The paper proposes a deep reinforcement learning approach for generating online missile-evading maneuvers for combat aerial vehicles. The problem is made complicated by the missile's 8 Mach speed and the aircraft's limited 2.5 Mach speed. The system employs Twin Delayed Deep Deterministic Policy Gradient(TD3), one of the most known deep reinforcement learning algorithms, to train an agent to make real-time decisions on the best evasion tactics in a complex combat environment. A two-term reward function is used, with sparse rewards at terminal states and continuous rewards through the geometry of the combat. Aileron, rudder, and elevator controls are given directly to the algorithm to ensure all potential escape maneuvers are visible. The proposed methodology achieved a 59% success rate in extensive simulations, demonstrating its potential to enhance aerial vehicles' combat capabilities.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9798350323023
DOI'lar
Yayın durumuYayınlandı - 2023
Etkinlik10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023 - Istanbul, Turkey
Süre: 7 Haz 20239 Haz 2023

Yayın serisi

AdıProceedings of 10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot7/06/239/06/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

Parmak izi

Missile Evasion Maneuver Generation with Model-free Deep Reinforcement Learning' araştırma başlıklarına git. Birlikte benzersiz bir parmak izi oluştururlar.

Alıntı Yap