An increasing number of space objects, debris, and satellites in Low Earth Orbit poses a significant threat of collisions during space operations. The situation is currently monitored by radar and radio-telescopes that track space objects, but much of space debris is composed of very small metallic objects that are difficult to detect. In a study published in IET Radar, Sonar & Navigation, investigators demonstrate the benefits of using deep learning-a form of artificial intelligence-for small space object detection by radar.
The team modelled a prominent radar system in Europe (called Tracking and Imaging Radar) in tracking mode to produce training and testing data. Then, the group compared classical detection systems with a You-Only-Look-Once (YOLO)-based detector. (YOLO is a popular object detection algorithm that has been widely used in computer vision applications.) An evaluation in a simulated environment demonstrated that YOLO-based detection outperforms conventional approaches, guaranteeing a high detection rate while keeping false alarm rates low.
"In addition to improving space surveillance capabilities, artificial intelligence-based systems like YOLO have the potential to revolutionize space debris management," said co-corresponding author Federica Massimi, PhD, of Roma Tre University, in Italy. "By quickly identifying and tracking hard-to-detect objects, these systems enable proactive decision-making and intervention strategies to mitigate collisions and risks and preserve the integrity of critical space resources."
URL: https://onlinelibrary.wiley.com/doi/10.1049/rsn2.12547