A Study on Event Identification for Small-sized Space Debris Based on Admissible Region

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K11945
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 61030:Intelligent informatics-related
• Research Institution: Nippon Bunri University
• Principal Investigator: Fujita Koki 日本文理大学, 工学部, 教授 (00315110)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: スペースデブリ観測 / 軌道上物体同定 / 軌道上物体特徴分類

Outline of Final Research Achievements

This study derives a method for classifying artificial space objects detected through radar or optical observation, focusing on fragmentation debris generated from explosion or collision of uncontrolled human-made objects in Earth orbits. In the framework of the proposed method, a constraining condition on orbital dynamics called Admissible Region is used to classify fragmentation debris with respect to a specific breakup event, which is combined with a machine learning algorithm based on Gaussian Mixture Model. That was finally verified with actual data obtained thorough some observation campaigns, comparing results of making direct correlations between observed data and known cataloged space objects.

Free Research Field

宇宙状況認識

Academic Significance and Societal Importance of the Research Achievements

人類の宇宙開発が進む中，宇宙空間における人工物体の混雑化がますます進む状況にあり，地上からの観測結果に基づき，それら人工物体群が既知・未知いずれのものであるか，また，どのような起源を有するものか同定を行ったり，破砕由来や軌道運動の特徴に基づいて分類することは，安全かつ持続的な宇宙環境利用にあたって意義が大きいと考えられる．特に本研究では，Admissible Regionと呼ばれる，軌道力学上の拘束条件から算出された地上観測では直接得られない不可観測量の存在可能領域を算出し，機械学習手法と組み合わせて，観測物体群に対する特徴分類手法の導出を行ったことに学術的意義がある．