To accurately evaluate the impact of blanking on pulse interception, this study examines the loss probabilities of pulses caused by the blanking.

The influence of blanking on pulse sorting was analyzed. Based on the pulse sorting method used by reconnaissance equipment, the criteria for identifying the loss pulses were established. The output waveform of a pulse erased by the blanking gate was then examined under various time-sequence relationships between the pulse and the blanking gate, allowing the varying law of the residual pulse to be obtained. The expressions were derived for the average loss probabilities of the pulses and the loss probabilities of the periodic pulses both caused by the periodic blanking gates, as well as for the average loss probabilities of the pulses under blanking gates with time-varying duty cycles. Finally, each pulse loss probability was validated by comparing the calculation results with numerical simulation. The maximum absolute deviation between the two was approximately 5.8×10⁻⁴, which is negligible.

The mathematical models for the pulse loss probabilities and the average loss probabilities are obtained, and their accuracy has been verified.

The research provides a quantitative evaluation of the influence of the blanking on the loss of the intercepted pulse. It provides the input for accurately analyzing the missed alarm probability in pulse interception with the blanking gate and supports decision-making regarding the use of blanking measures.

As global warming accelerates the melting of sea ice, the Arctic region witnesses an increase in ship navigation. The brash ice area, composed of brash ice of various sizes and shapes, is a common operational scenario for polar ships. Understanding the ice load characteristics of polar ships during oblique navigation in brash ice regions is crucial. This can enhance ship navigation safety in the complex polar marine environment, provide a reference for polar navigation route planning, and fill the gap in the current research that mainly focuses on straight-sailing conditions.

This study selects a specific type of polar ship as the research object and utilizes the discrete element method (DEM) to predict the ice loads on the ship during oblique navigation through brash ice regions. First, a numerical model of the target ship is established. The model parameters include a ship model with a scale ratio of 60, a total length of 2.04 m, a beam of 0.37 m, and a design draft of 0.13 m. The ice particles have a density of 917.0 kg/m³, a Poisson's ratio of 0.3, and other specific properties. The accuracy of the model is verified by comparing it with the experimental results from the literature under the straight-sailing condition. Then, different oblique-sailing angles (0° −15°), speeds (0.6, 0.7 m/s), and ice thicknesses (0.011 67, 0.014 97 m) are set. The ice-load calculation is carried out based on the momentum conservation equation, angular momentum conservation equation, and the linear spring contact force model in the DEM.

The results show that as the drift angle increases, the ice-breaking resistance and lateral force on the ship increase non-linearly. For example, at a speed of 0.6 m/s, an ice concentration of 70%, and an ice thickness of 0.014 97 m, when the drift angle is 15°, the ice-breaking resistance and lateral force increase by 4.25 times and 6.04 times respectively, compared to the straight-sailing condition. In terms of speed, when the drift angle is between 0° and 10°, the ice-breaking resistance increases slowly, but when it exceeds 10°, it increases significantly. The lateral force also increases non-linearly, and the influence of speed on the lateral force is more significant than whether the ship is on the ice-facing side. Regarding the influence of ice thickness, when the drift angle is greater than 10°, the ice-breaking resistance and lateral force increase significantly as the ice thickness increases.

In conclusion, this research provides reliable data support for the safety assessment of ships during oblique navigation in polar brash ice regions. It offers a valuable reference for predicting and studying ice loads on polar ships under such conditions. Ship operators should be cautious when increasing speed or entering thicker ice areas, especially when the drift angle is greater than 10°. This is to avoid potential risks caused by sudden changes in ice-breaking resistance and lateral force, ensuring the safe and stable navigation of polar ships in complex ice-covered waters.

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 challenge of low fault diagnosis accuracy in traditional neural networks with few labeled samples, a method based on contrastive learning and convolution transformer network is proposed.

First, raw monitoring data are transformed into similar sample pairs by data augmentation. These similar sample pairs are then mapped to a deep feature space by a feature extractor. A transformer network is utilized to design cross-prediction tasks for both local and global comparisons, facilitating the clustering of data with the same fault type by comparing the intrinsic similarity between the same batches of data. Finally, the downstream classification network is trained with few labeled samples to improve the diagnostic performance of the proposed model.

The effectiveness of the proposed method is validated using a self-built reducer test rig. The results show that accuracy of the proposed method reaches 98.38% with few labeled samples, showing significant advantages over existing methods.

The research results can provide the key technology for fault diagnosis of industrial equipment with few labeled samples, contributing to the advancement of intelligent manufacturing.

To suppress hydrodynamic noise at the source, a noise reduction method for pump-jet propulsors based on porous media is proposed.

By replacing the metallic leading edges of the stator blades of the pump-jet propulsor with porous materials, the interaction between the blade wake and the inner wall of the duct can be effectively modulated, thereby reducing wall pressure fluctuations. Large eddy simulation (LES), combined with acoustic analogy analysis, was employed to investigate the flow characteristics and noise control performance of the stator blades with porous leading edges. The mechanisms by which the porous media modulates the flow field and suppresses noise were analyzed, and the effects of key parameters, such as porosity and advance coefficient, on hydrodynamic noise control were examined.

Comparative results indicate that the porous leading edges of the stator significantly reduce the low-frequency sound pressure level components on the duct wall and the far-field radiation noise. The maximum reduction in the sound pressure level (SPL) reaches 5.52 dB in the direction perpendicular to the rotation axis of the pump-jet propulsor.

The findings of this study provide useful guidance for flow control and hydrodynamic noise reduction in pump-jet propulsors.

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 underwater dynamic navigation based on the sectional observation system generates multi-source and heterogeneous data, creating crossed or forked tracks due to asynchronous time delay and unknown system errors. This makes it difficult to represent continuous navigation processes and identify local characteristic points. To address this issue, a functional reconstruction algorithm for underwater data fusion is proposed.

The polynomial constraint fusion (PCF) method and the spline function fusion (SFF) method are employed to process track data collected via sectional observations. These methods effectively integrate the full underwater track and address issues such as discontinuous dynamic parameter sequences and ambiguous data in overlapping section.

Numerical simulations show that both PCF and SFF methods can capture the main characteristics of underwater dynamic motion and produce accurate and continuous tracks. Compared with the general data fusion (GDF) method, the PCF and SFF yield smoother and more continuous data series, enabling a more precise representation of motion in overlapping regions. Compared with the moving average filter algorithm, the fusion processing results based on the functional reconstruction algorithm and the filter algorithm both show an optimizing performance in accuracy and smoothness. In terms of velocity and acceleration consistency, the functional reconstruction algorithm is better than the filter algorithm. Verified by sea trials, the SFF and the PCF were used to obtain the re-analysis track in the observed section with velocity estimation errors within 5% at the characteristic points, and also obtain the predicted track in subsequent sections with errors within 15%.

The proposed method shows application values for processing multi-source and heterogeneous data in complex underwater motion scenarios, and is also effective for the short-term underwater navigation estimation.

This paper investigates the high-precision control challenges associated with the autonomous recovery of an autonomous underwater vehicle (AUV) by a dynamically moving docking base. During the docking process, the recovery performance is significantly affected by complex underwater environments, including time-varying external ocean currents and inherent model uncertainties. To address these challenges, this study aims to propose a robust double-loop control strategy designed to achieve rapid, stable, and precise pose alignment between the AUV and the moving docking base under constrained conditions.

Using the "White Dolphi 100" docking system as the primary research platform, a 5-DOF motion model is established to formulate the dynamic docking problem. The proposed control architecture consists of an outer kinematic loop for pose error regulation and an inner dynamic loop for velocity tracking, utilizing an adaptive fast nonsingular integral terminal sliding mode control (AFNITSMC) strategy. Specifically, a fast nonsingular integral terminal sliding mode surface is designed to ensure finite-time convergence of the system states while effectively eliminating the singularity issues inherent in conventional terminal sliding mode control methods. To enhance robustness, an adaptive lumped disturbance estimation law is incorporated to online estimate and compensate for uncertainties—such as model parameter mismatches and time-varying ocean currents—without requiring any prior knowledge of the disturbance upper bounds. Furthermore, a boundary layer technique is introduced into the switching term of the control law to mitigate the chattering phenomenon, thereby protecting the mechanical actuators. The stability and finite-time convergence of the overall closed-loop system are rigorously established using the Lyapunov stability theory.

Extensive simulation studies were conducted using the hydrodynamic parameters of the "White Dolphin 100" docking system to validate the effectiveness of the proposed control method. The simulation scenarios accounted for 20% thrust saturation limits, time-varying ocean current disturbances, and 20% perturbations in model parameters. The results indicate that the AFNITSMC method achieves rapid pose convergence within 10 seconds, with specific convergence times of 4.6, 7.0 and 9.39 s in the longitudinal, lateral, and vertical directions, respectively. This performance significantly surpasses that of the baseline nonsingular integral terminal sliding mode control (NITSMC), which required much longer intervals to stabilize. Regarding steady-state accuracy, the mean absolute errors (MAE) for position were measured at 0.142, 0.103, and 0.0397 cm, while the attitude errors were 0.012° and 0.054°. Compared to the NITSMC method, the proposed method reduced position errors by 75.7%, 87.6%, and 95.3%, and attitude errors by 96.5% and 62.2%, demonstrating its superior tracking precision and robustness.

The proposed AFNITSMC exhibits excellent control performance and promising engineering application prospects in addressing the dynamic base docking problem under external disturbances and model uncertainties.