Abstract
Marine target classification is a key technology for unmanned surface vehicles (USVs) to perform ocean surveillance. Traditional maritime target classification methods require improvements in both accuracy and processing speed when handling classification tasks. In this paper, a distributed maritime target classification (DMTC) method based on broad learning and MobilityFirst is proposed. Firstly, a multi-model collaborative classification and fusion framework is proposed to achieve feature consistency fusion. Secondly, to enhance the security and privacy of communication in autonomous surface vehicles, the MobilityFirst approach is employed to improve information complementarity among multiple models within the distributed framework. Finally, the broad learning system, as the model’s classification layer, reduces the training complexity. Extensive experimental results demonstrate that this proposed approach surpasses single-model and distributed methods in accuracy, F1 score, and the area under the precision–recall curve (AUPR). This approach offers a clear advantage in multi-ship classification tasks while simultaneously enhancing the model’s generalization capability.