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Beijing Institute of Technology
2024
Air Force Engineering University
2023
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The self-organization capabilities of massive aerial swarms pose challenges to conventional methods grouping targets. These traditional approaches struggle with issues such as stability, reliability, and the recognition deep intentions. To overcome these challenges, we propose an architecture called Unsupervised Contrastive Learning for Aerial Targets Grouping (UCL-ATG). Our approach utilizes random time periods within scenario training batches contrastive learning. UCL-ATG model consists...