Federated Learning-Enabled Self-Reconfigurable Satellites for Resident Space Objects Detection

As the space environment becomes more complex, efficient detection and tracking of resident space objects (RSOs) in Earth’s orbit is increasingly important. Current single-satellite detection systems are, however, limited by observation range and computational power, making real-time RSO awareness difficult. In order to address this challenge, we propose a collaborative detection model with reconfigurable satellites that leverages a federated-learning framework to enable situational awareness and object detection across multiple nodes. Specifically, we designed a modular self-reconfigurable satellite (MSRS) consisting of four modules, with each module equipped with two cameras. By stitching together their fields of view, the constellation offers comprehensive situational awareness of the surrounding $2\pi $ annular space. The approach overcomes the limitation of single-point computing power by aggregating and updating network parameters among reconfigurable satellites, effectively addressing the risks associated with multimodules data transmission. We conduct extensive experiments to validate the effectiveness of our proposed reconfigurable satellite in detecting RSOs. Experimental results show that the optical system constructed in this article can effectively perceive sixth-magnitude stars with an exposure time of 1 s, and the proposed detection model exhibits a maximum increase in accuracy of 5.2% compared with the single satellite model.