Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Author:

Massimi Federica1,Ferrara Pasquale2ORCID,Petrucci Roberto3,Benedetto Francesco1ORCID

Affiliation:

1. Signal Processing for Telecommunications and Economics Lab Roma Tre University Rome Italy

2. Leonardo Labs Leonardo Spa Rome Italy

3. Electronics ITA Business Unit – Engineering Leonardo Electronics Rome Italy

Abstract

AbstractThe increasing number of space objects (SO), debris, and constellation of satellites in Low Earth Orbit poses a significant threat to the sustainability and safety of space operations, which must be carefully and efficiently addressed to avoid mutual collisions. The space situational awareness is currently addressed by an ensemble of radar and radio‐telescopes that detect and track SO. However, a large part of space debris is composed of very small and tiny metallic objects, very difficult to detect. The authors demonstrate the benefits of using deep learning (DL) architectures for small space object detection by radar observations. TIRA radio telescope has been simulated to generate range‐Doppler maps, then used as inputs for object detection exploiting You‐Only‐Look‐Once (YOLO) frameworks. The results demonstrate that the object detection by using YOLO algorithms outperform conventional target detection approaches, thus indicating the potential benefits of using DL techniques for space surveillance applications.

Publisher

Institution of Engineering and Technology (IET)

Reference47 articles.

1. UCS Satellite Database.https://www.ucsusa.org/resources/satellite‐database#.W7WcwpMza9Y accessed 8 May 2023

2. ESA:Space Environment Statistics: Space Debris by the Numbers.https://sdup.esoc.esa.int/discosweb/statistics/. accessed 2 May 2023

3. LEO Mega-Constellations for 6G Global Coverage: Challenges and Opportunities

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