To address the problem of inadequate path-following accuracy and stability in unmanned surface vehicles (USVs) operating in complex environments (characterized by uncertainties such as fluctuating wind speeds and initial position deviations), a guidance method called time-varying sideslip compensated adaptive line-of-sight (TSC-ALOS) is proposed.

First, a time-varying sideslip compensation mechanism is introduced based on real-time measurements of wind speed and direction, which forms the foundation of the improved TSC-ALOS algorithm. This mechanism dynamically compensates for sideslip angle variations induced by environmental disturbances, thereby optimizing the desired heading output of the USV. Subsequently, a proportional-derivative (PD)-based heading controller is designed. This controller translates the desired heading generated by the TSC-ALOS algorithm into actual rudder angle commands, enabling the USV to rapidly and stably track the target heading. This also establishes an effective connection between high-level navigation strategies and low-level control execution. Finally, numerical simulations emulating real marine environments are conducted. The performance of TSC-ALOS algorithm is compared with that of adaptive LOS (ALOS) and traditional LOS algorithms under three operational conditions: no wind, steady wind, and variable wind. Key metrics such as cross-track error and heading stability are specifically analyzed.

Simulation results demonstrate that under no-wind conditions, both TSC-ALOS and ALOS algorithms achieve higher path-following accuracy than traditional LOS algorithm, particularly in handling turning segments. Under steady wind (wind speed: 8.37 m/s) and variable wind (wind speed: 16.73 m/s) conditions, TSC-ALOS significantly reduces the cross-track error, showcasing stronger resilience to environmental disturbances. In scenarios with initial position deviations, the average cross-track error of TSC-ALOS is reduced by 24.6% and 36.8% compared to ALOS and LOS algorithms, respectively.

The TSC-ALOS algorithm demonstrates superior guidance performance across various complex environments, with particularly notable advantages in addressing environmental disturbances and initial position deviations. It offers essential technical support for the development of autonomous navigation systems for USVs and provides insights into future research directions for algorithm optimization.

To address the challenges of preventing non-random multiple concurrent faults caused by cable aging in shipboard power grids through preventive reconfiguration, and to resolve the issue of unreasonable weight coefficient settings in multi-objective reconfiguration models, thereby enhancing the safety and reconfiguration efficiency of shipboard power grids, a predictive fault reconfiguration method for shipboard power grids based on a double-level optimization strategy is proposed.

A cable aging fault prediction model for shipboard grids was constructed based on Markov chains and thermo-electro-mechanical multi physics analysis. This model was integrated as a constraint into the reconfiguration framework to avoid high-risk branches. A dual-layer optimization strategy was proposed: the upper layer dynamically solves multi-objective weight coefficients using the whale migration algorithm (WMA), while the lower layer determines the optimal switch configuration for grid reconfiguration using a multi-strategy-improved dung beetle optimizer (MSDBO).

After integrating the fault prediction model, the reconfiguration strategy achieved 100% avoidance of high-risk branches (fault probability ≥0.5) proactively. Compared to the conventional two-step passive reconfiguration strategy, convergence speed improved by 47.06%. The dual-layer optimization framework enabled adaptive dynamic adjustment of weight coefficients and increased reconfiguration convergence speed by 56.25%.

The integration of the cable aging fault prediction model and the dual-layer optimization framework effectively enables predictive reconfiguration of shipboard power grids. This approach proactively mitigates non-random faults while significantly improving reconfiguration efficiency and rationality. It offers a novel solution for addressing predictive reconfiguration challenges in non-random multiple-fault scenarios.

This paper introduces a novel data-driven approach for generating realistic and hazardous overtaking scenarios. These scenarios are crucial for rigorously evaluating the autonomous collision avoidance capabilities of autonomous ships. Existing methods often struggle to balance scenario diversity, realism, and the representation of hazardous situations. To overcome this limitation, our method leverages the rich information embedded in automatic identification system (AIS) data to generate diverse and realistic overtaking encounters.

Specifically, we propose a hybrid model that integrates a sequence generative adversarial network (SeqGAN) with a self-attention mechanism (SAM). The SeqGAN captures the complex patterns and dynamics in AIS-based ship trajectories, enabling the generation of novel, yet plausible, overtaking maneuvers. The incorporation of a SAM further enhances the model's ability to capture long-range dependencies in ship trajectories, resulting in more realistic and nuanced simulations. To ensure that the generated scenarios accurately reflect hazardous situations, we have developed a constraint model based on longitudinal and lateral safety distances between vessels to define realistic initial conditions. This model dynamically adjusts the initial positions and velocities of both the target vessel and the autonomous ship under test, ensuring that each generated scenario presents a genuine collision risk.

The results show that the effectiveness of our approach is validated through extensive simulations. A total of 500 hazardous overtaking scenarios were generated, significantly improving the coverage of test scenarios. Notably, 97.3% of these generated trajectories fall within a predefined buffer zone that encompasses real-world trajectories, demonstrating the high fidelity of our model. Furthermore, the speed distributions of the generated target vessels closely match those observed in real-world AIS data, further validating the realism of our approach.

The enhanced realism and diversity of scenarios generated by this method significantly improve the efficiency of autonomous collision avoidance testing. This allows for a more precise definition of safety performance boundaries and accelerates the development and optimization of autonomous collision avoidance algorithms. Ultimately, this work contributes to the development of safer and more reliable autonomous maritime systems capable of navigating the complexities of modern maritime environments.

This research focuses on the energy characteristics of false targets generated by time-modulated adaptive jamming technology, aiming to investigate the significant variations in these characteristics caused by different modulation schemes and parameter settings. It aims to provide a comprehensive understanding of how modulation parameters influence the energy distribution of false targets, thereby offering practical insights for electronic warfare applications.

First, theoretical interference models were established for different modulation schemes against linear frequency modulation (LFM) pulse radar. These models elucidate the mapping relationship between modulation timing and the amplitude of false targets. Second, a Ku-band jamming system was designed and built to experimentally validate the theoretical findings. The system incorporates 1-bit modulation and control modules to generate time-modulated signals. Numerical simulations were conducted to evaluate the energy characteristics of false targets under various duty cycles and modulation schemes. Additionally, experimental measurements were performed in a controlled environment to compare the performance of various modulation modules and to verify the accuracy of the simulation results.

The results demonstrate that 1-bit modulation effectively conceals the target's energy at the fundamental frequency, making it difficult for radar systems to detect the true target. Under a fixed modulation scheme, it was observed that decreasing the duty cycle of the modulation signal reduces the amplitude difference between each harmonic and the fundamental frequency. When the harmonics approach the fundamental frequency in amplitude, the radar's ability to distinguish between true and false targets is significantly compromised. This finding highlights the importance of optimizing the duty cycle to enhance the effectiveness of time-modulated adaptive jamming. The experimental results closely matched the numerical simulations, validating both the theoretical models and the effectiveness of the proposed jamming system.

By employing 1-bit modulation and carefully adjusting the duty cycle of the modulation signal, it is possible to effectively shape the energy profile of the real target, thus improving the jamming effectiveness against modern radar systems. This research provides both qualitative and quantitative analysis of the energy characteristics of false targets, and offers practical guidance for the development and implementation of time-modulated adaptive jamming systems. Future work may focus on extending this study to multi-target jamming scenarios, and on incorporating artificial intelligence algorithms to optimize jamming strategies in real time, as well as exploring countermeasures against emerging radar technologies.

Complex backgrounds, significant target size variations, and severe sea clutter in maritime infrared imagery often result in missed or false detections. To address this challenge, an improved method based on YOLOv8n, termed maritime infrared target detection-YOLO (MITD-YOLO), is proposed to enhance target detection accuracy in maritime infrared images.

MITD-YOLO incorporates a diverse branch module (DBB) and enhanced multi-scale convolution (EMSConv) to leverage multi-scale convolutions, enabling the model to more effectively capture complex features. A triple attention mechanism is employed to facilitate spatial and channel-wise feature interaction, thereby improving key feature extraction. Additionally, the powerful-IoUv2 (PIoUv2) loss function is introduced to address the anchor box expansion problem, leading to improved detection accuracy and enhanced model robustness.

Experimental results show that the improved model significantly enhances the efficiency of maritime infrared target detection, with a 2.3% increase in precision and a 1.7% increase in recall. The model achieves an average precision of 88.9%, and 132.8 FPS, outperforming the original model.

MITD-YOLO enhances maritime infrared target detection performance and provides a more reliable target detection technology for applications such as maritime surveillance and ship navigation, contributing to the advancement of intelligent maritime systems.

The information obtained through forced detection is often inaccurate, and targets frequently change course unpredictably. This degrades the performance of target maneuver detection and hampers the analysis of the target motion pattern. Therefore, this paper proposes a detection method for maneuvering maritime targets based on prior knowledge.

The method incorporates two types of prior knowledge derived from expert experience. The first is that significant differences in target heading occur before and after maneuvering, whereas the target heading remains relatively stable during non-maneuvering periods. The second is that the heading difference before and after maneuvering reaches a local extremum. The maneuvering point in the trajectory tends to maximize the heading difference between adjacent sub-trajectories. Based on the definition of trajectory smoothness metric, a calculation method is proposed to calculate the course maneuver evaluation factor based on principal component analysis (PCA). This factor enables preliminary screening of potential maneuvering points. In order to find trajectory points that satisfy the second prior knowledge, a maximum filtering-based maneuvering point screening method is proposed.

Simulation results show that, compared with the mainstream interactive multiple model (IMM) algorithm and information entropy-based algorithm, the target maneuver inflection points detected by the proposed method are closer to the actual inflection points, with the lowest false detection rate and missed detection rate. Moreover, when track compression is performed using the maneuver positions extracted by this method, the distance error relative to the original track is minimized.

The findings confirm the superiority of the proposed algorithm, which can effectively improve the accuracy and robustness of target maneuver detection and provide strong support for target behavior analysis and operational decision-making at sea.