Space Debris Characterization Using Machine Learning Methods

The project titled "Space Debris Characterization Using Machine Learning Methods" addresses the increasing threat of orbital debris, with NASA tracking over 23,000 objects larger than 10 cm and estimates suggesting millions more smaller pieces. The proposal highlights the need for improved models to predict the damage caused by collisions with space debris, emphasizing the importance of mass, density, speed, and shape in these predictions. Current methodologies, primarily using basic shape characteristics, are deemed insufficient, prompting a shift towards advanced classification techniques involving 3D scanning and machine learning. The project aims to use high-resolution scans to gather detailed shape characteristics and apply machine learning methods like Principal Component Analysis and K-Means Clustering for better data processing and classification. The background stresses the urgency of addressing orbital debris, particularly in Low Earth Orbit, where collisions are becoming more frequent due to the accumulation of satellites. The project also notes that existing models, such as those from the NASA DebriSat program, lack sufficient detail for advanced analysis. The proposed approach will allow for rapid adaptation to changes in satellite designs and debris characteristics, making it more efficient than traditional methods. Safety precautions for handling debris are outlined, and outreach activities are planned to share research findings. Ultimately, this research aims to enhance understanding of the orbital debris environment and develop strategies to mitigate future debris generation, benefiting both the research community and broader space operations.

This content was originally posted on my projects website here. The above summary was made by the Kagi Summarizer