Air-Launched Weapon Engagement Zone Development Utilizing SCG (Scaled Conjugate Gradient) Algorithm

• 영문명: Air-Launched Weapon Engagement Zone Development Utilizing SCG (Scaled Conjugate Gradient) Algorithm
• 발행기관: 한국인공지능학회
• 저자명: 조한상(Hansang JO) Rho Shin Myong
• 간행물 정보: 『인공지능연구』Vol.12 No. 2, 17~23쪽, 전체 7쪽
• 주제분류: 복합학 > 과학기술학
• 파일형태: PDF
• 발행일자: 2024.06.30

영문 초록

Various methods have been developed to predict the flight path of an air-launched weapon to intercept a fast-moving target in the air. However, it is also getting more challenging to predict the optimal firing zone and provide it to a pilot in real-time during engagements for advanced weapons having new complicated guidance and thrust control. In this study, a method is proposed to develop an optimized weapon engagement zone by the SCG (Scaled Conjugate Gradient) algorithm to achieve both accurate and fast estimates and provide an optimized launch display to a pilot during combat engagement. SCG algorithm is fully automated, includes no critical user-dependent parameters, and avoids an exhaustive search used repeatedly to determine the appropriate stage and size of machine learning. Compared with real data, this study showed that the development of a machine learning-based weapon aiming algorithm can provide proper output for optimum weapon launch zones that can be used for operational fighters. This study also established a process to develop one of the critical aircraft-weapon integration software, which can be commonly used for aircraft integration of air-launched weapons.

목차

1. Introduction

2. Development of Machine Learning-based Weapon Aiming Algorithm

3. Algorithm Generation

4. Algorithm Development and Verification

5. Conclusion

References

키워드

Air-launched Weapon Mission System Machine Learning Weapon Engagement Zone SCG Scaled Conjugate Gradient