인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study presents a two-stage algorithmic framework for target/non-target identification and projectile classification using measurement data from counter-battery radar systems. The first stage focuses on distinguishing ballistic targets—characterized by their predictable trajectories—from non-targets exhibiting autonomous or irregular flight patterns. A trajectory-fitting approach is applied to the radar measurements to evaluate their consistency with ballistic motion models, enabling robust discrimination between targets and non-targets. Once a ballistic target is successfully identified, the second stage estimates its trajectory using a modified data-fitting method that minimizes the residuals of both the measurement and dynamic models. The target's motion is approximated by a polynomial function of time, and the resulting trajectory is used to infer the projectile type. Classification is then performed based on an estimated dynamic parameter, composed of mass, reference area, and drag coefficients, derived from the fitted trajectory. Both identification and classification algorithms rely on data-fitting techniques that capture the underlying dynamics and extract key parameters inherent in counter-battery radar observations. The effectiveness of the proposed method is verified through numerical simulation. The proposed method is expected to enhance the radar system’s ability to autonomously filter threats and classify incoming projectiles with minimal prior information.
본문·목차
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UCI(KEPA) : I410-151-25-02-094004528
