To meet the requirements of formation control and maneuverability in multiple unmanned surface vehicle (USV) systems operating in irregular environments, a distributed cooperative maneuvering target tracking control method based on affine transformation is proposed.

First, a novel output redefinition-based dynamic transformation method is proposed to address the absence of relative degree in underactuated USV systems, thereby simplifying the control design process. Subsequently, a distributed target velocity observer is designed to estimate the unknown velocity of moving targets. Based on the estimated target velocity, a distributed target tracking control strategy is developed by integrating affine transformation theory with backstepping control to enable agile tracking of moving targets. To address unknown model parameters and external disturbances, an adaptive law is constructed using the minimum learning parameter (MLP) technique and radial basis function neural network (RBFNN) for online estimation.

Simulation results demonstrate that the proposed control method enables effective tracking of moving targets in irregular environments. When targets navigate through narrow passages, the multi-USV system flexibly adjusts its formation to avoid obstacles, exhibiting both robustness and flexibility.

The proposed affine transformation-based control method enhances both the flexibility and safety of multi-USV systems in tracking moving targets in irregular environments. This work provides a significant advancement in distributed cooperative control strategies for multi-USV systems and offers practical value for maritime surveillance and rescue operations.

Aiming at the problem of ship hull number recognition, this paper proposes a real-time ship's hull number recognition method for unmanned surface vehicles (USVs).

Based on a one-stage object detection model (e.g. YOLO), the attention mechanism is introduced to make the network more sensitive to the target area by the spatial information interaction module and divided attention method. Considering the effect of prior knowledge on accuracy, the adaptive anchor method and positive sample assignment strategy are utilized to improve the accuracy of regression. Aiming to resolve the problem of slow convergence at the beginning, the loss function is redesigned to speed up the convergence and enhance the stability of the network in the training phase. Finally, the proposed method is deployed in a USV to validate the availability of the recognition performance.

The results shows that the proposed method can achieve the recognition of ships and hull numbers simultaneously under Sea State 3 conditions, and has a 14% improvement in mean average precision (mAP) compared with the original model, with the ability to perform recognition in real time.

The results of this study indicate that the proposed method can be applied to USVs to perform hull number recognition, even under complex ocean conditions.