In this paper, a two-level visual servo strategy is elaborately devised for an unmanned surface vehicle (USV) equipped with a pan-tilt camera, so as to exactly track the desired trajectory around a visual target without direct position measurements. In the lower level, a barrier function-based adaptive pseudo-inverse (BFAP) controller is specially designed for the camera to keep the target in sight. Together with a finite-time position observer (FPO) and a finite-time extended state observer (FESO), a model-free finite-time trajectory-tracking control (MFTC) scheme is naturally synthesized for the USV on the higher level. Prominent advantages are presented as follows: 1) The BFAP controller can not only circumvent the singularity issue in a simpler manner, but also solve the field-of-view problem thoroughly in spite of unknown image depth; 2) The FPO provides a new vision-based method to locate the USV by rapidly calibrating a constant extrinsic parameter of the camera online, achieving higher positioning accuracy; and 3) The MFTC scheme allows all model information of the USV to be unknown, which is more favorable to practical implementations. Stability analyses are strictly made by the Lyapunov theory, and simulation studies conducted on the prototype CyberShip II comprehensively demonstrate remarkable performance of the proposed BFAP controller and MFTC scheme.

With the continuous progress of contemporary science and technology and the increasing requirements for marine vehicles in various fields, the intelligence and automation of ships have become a general trend. The autonomous control of surface Unmanned Surface Vessel (USV) generally covers the USV path planning, path tracking control, and autonomous collision avoidance control. But in the whole navigation process of USV, autonomous berthing is also a crucial part. And the research on the algorithm of the automatic berthing process of the USV is less. Mature USV autonomous berthing technology can effectively reduce the cost of human and material resources and financial resources while reducing the accident rate reasonably and safely. Therefore, it is of great importance to comprehensively promote the development of USV autonomous berthing technology.

Recently, significant advancements have been achieved in optimizing algorithms for synthetic aperture radar (SAR) ship detection. Nevertheless, two challenges still impede further research: 1) Mainstream methods, whether anchor-free or anchor-based, adhere to a dense paradigm, leading to substantial redundancy and limited adaptability. 2) Ship targets in SAR images exhibit large shape variations and scale differences, making it difficult to efficiently extract key features from background clutter. To tackle the aforementioned problems, we propose DCEA (DETR with Concentrated dEformable Attention), a query-based method for end-to-end optimization of the current pipeline. First, for the irregular shapes and sparse distribution of ships, the concentrated deformable attention (CDA) is introduced to model the spatial positions of targets, simulating their geometric transformations with precision. Second, an attention-wise propagation module (APM) is designed to integrate local fine-grained information with global semantic information, improving the detection performance for objects across diverse scales. Finally, due to the lack of information exchange between object queries, a dimension-wise information mixing (DIM) module is employed to incorporate key information from various dimensions to enhance their representation capability. To validate the superior performance of DCEA, we conduct extensive experiments on multiple public datasets, achieving mean average precision (mAP) scores of 0.991, 0.929, and 0.962 on the SSDD, HRSID, and SAR-Ship-Dataset, respectively, with a model size of only 14.34M parameters and 44.4 GFLOPs.

Underwater acoustic Internet of Things Networks (UAIoTNs) play a crucial role in oceanographic and environmental monitoring, necessitating precise localization for optimal functionality. However, the underwater setting introduces significant challenges, encompassing the stratification effect arising from underwater heterogeneity, uncertainty in anchor positions due to currents, and variations in the signal transmission environment. These factors collectively impede the accurate estimation of location. Consequently, this paper addresses these challenges by analyzing and deriving a closed-form solution using a time-of-arrival (TOA)-based technique for 3D localization in UAIoTNs. The investigation establishes an underwater stratified propagation model, drawing inspiration from ray tracing theory and Snell’s law. Employing the Cramér-Rao lower bound (CRLB) framework, we explore scenarios both with and without considering perturbed anchors, utilizing the Banachiewicz-Schur theorem. To quantify the impact of the stratification effect and perturbed anchors on CRLB and mean square error (MSE), we further analyze and derive an MSE expression, employing Taylor-series linearization. Building on our analysis of the detrimental effects of stratification and inaccurate anchors, we introduce a multiple-weighted least squares (MWLS) algorithm to alleviate potential performance losses. This approach integrates a matrix operator in the update step, eliminating variable dependencies and resulting in a closed-form solution that circumvents the need for iterative processes. Our simulation results validate our analytical findings and demonstrate the effectiveness of the proposed method, showcasing improved localization accuracy across various scenarios when compared to state-of-the-art approaches.