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.

To address the low docking accuracy of autonomous underwater vehicles (AUVs) in complex underwater environments, a multi-feature fusion vision-based method is proposed.

A self-developed rudderless vector propulsion AUV with four thrusters was used, and the dark channel prior (DCP) dehazing algorithm was adopted for image enhancement. An improved Canny edge detection algorithm was combined with color threshold segmentation to achieve multi-feature fusion. The minimum enclosing circle method was utilized for circle center positioning, and coordinate transformation was performed to calculate the relative position and orientation for docking.

Unity 3D simulations and pool experiments revealed a distance-dependent trend: both mean difference and root mean square error decreased as docking distance decreased. Closer distances yielded higher visual ranging accuracy and docking precision. When the docking distance was less than 2 m, the positioning error was maintained below 5 cm, with an overall success rate of 88%.

The proposed method fulfills the accuracy requirements for AUV autonomous docking and provides a highly robust solution for underwater equipment recovery.

To address frequent actuator failures caused by complex underwater environments and the inherent characteristics of unmanned underwater vehicles (UUVs), this study investigates a prescribed performance path-following fault-tolerant control scheme for an underactuated UUV subject to ocean current disturbances, model uncertainties, and actuator faults. To ensure safe UUV navigation, a path-following fault-tolerant controller is designed by integrating an improved prescribed performance function with a barrier Lyapunov function, enabling full-state-constrained fault-tolerant control.

A novel predefined-time disturbance observer is developed to estimate the lumped disturbances arising in UUV path-following, including ocean currents, parameter perturbations, unmodeled dynamics, and thrust loss caused by actuator faults. The lumped uncertainties with actuator faults are incorporated into the prescribed performance fault-tolerant controller for compensation, ensuring that all path-following state errors remain within predefined bounds.

Simulation results demonstrate that the position error, attitude angle error, and angular velocity error converge rapidly while strictly satisfying the prescribed safety constraints, achieving a steady-state position error bound of 1 meter and an attitude angle error bound of 0.05 radians. When the actuators suffer up to 80% thrust loss, the disturbance observer rapidly estimates the lumped disturbances, and the controller compensates for the faults within 1 second without significant path-following deviation. The maximum transient error does not exceed 20% of the prescribed limit. These findings validate the strong robustness of the proposed method against actuator faults. By unifying disturbance observation with prescribed performance constraints, the fault-tolerant control structure is simplified, achieving both fast fault response and full-state safety guarantees.

This work provides a universal solution for high-performance UUV navigation in complex underwater environments.

To address the challenges in multi-AUV formation maneuvering, such as limited state perception and transmission capabilities, acoustic communication delays, data loss, and reduced observability due to the lack of position information exchange, this study proposes an event-triggered metrology−communication unified framework with a Lyapunov-based model predictive formation control method (ETMCU−LMPC). The proposed approach aims to enhance formation stability and tracking accuracy.

First, by integrating the formation communication topology with system states, an event-triggered mechanism based on state observation is established. This mechanism leverages relative measurements among AUVs to mitigate delays and data loss caused by acoustic communication failures, while improving system observability in the absence of position information exchange. Second, a distributed model predictive controller based on Lyapunov theory is designed. The controller employs backstepping to construct contractive constraints, ensuring recursive feasibility, and incorporates adaptive Kalman filtering (AKF) to compensate for measurement noise, thereby guaranteeing closed-loop stability.

Simulation results of the formation control for five AUVs (1 leader and 4 followers) show that, compared with the traditional LMPC, the proposed ETMCU−LMPC method reduces the convergence time from 8 s to 6 s, the maximum error from 1.12 m to 0.36 m, and the steady-state error from 0.57 m to 0.06 m. Additionally, the control input exhibits greater stability.

The proposed method can effectively cope with communication anomalies, improve the reliability of multi-AUV formations under scenarios with limited state perception and transmission, and thus possesses practical engineering significance.

This study aims to investigate the dynamic behavior and flow field characteristics of trans-medium submersibles during underwater straight-line navigation and turning maneuvers.

To this end, computational fluid dynamics simulations were employed, using the VOF multiphase flow model and the SST k–ω turbulence model to establish a numerical model of the underwater navigation of the trans-medium submersibles. The accuracy of the numerical method was validated by comparing the experimental total drag data for the DARPA Suboff submarine model at various speeds with the numerical calculation results. On this basis, numerical simulations and analyses of underwater straight-line navigation and turning maneuvers of the trans-medium submersible were conducted, focusing on the effects of ducted propeller rotation speed and tail fin deflection angle on the underwater straight-line navigation and turning performance of the submersible.

The research results indicate that during straight-line underwater navigation, the forward speed of the trans-medium submersible exhibits an approximately linear relationship with the propeller's rotational speed. For instance, as the propeller speed increases from 600 r/min to 4800 r/min, the forward speed rises from 1.1 m/s to 8.1 m/s. At the same time, the pitch moment becomes less negative with increasing propeller speed (from −0.35 N·m to −0.17 N·m), indicating that the submersible remains stable in pitch during high-speed navigation. The propeller speed has little effect on the surface pressure distribution and the structure of the surrounding flow field. During underwater turning, the turning radius is mainly determined by the tail fin deflection angle and is largely unaffected by the propeller speed. The turning radius decreases with increasing tail fin deflection angle (from 3.35 times the submersible's body length to 0.75 times), though the rate of decrease diminishes. In contrast, the turning speed is affected by both the propeller speed and the tail fin deflection angle. The thrust generated by both propellers increases with higher propeller speeds and larger tail fin deflection angles. During turning, the thrust of the outer propeller consistently exceeds that of the inner propeller, and the thrust difference increases with greater tail fin deflection. Furthermore, tail fin deflection during turning leads to a significantly asymmetric surface pressure distribution on the submersible. This asymmetry becomes more pronounced with increasing tail fin deflection and is closely associated with the asymmetric flow characteristics of the surrounding flow field.

This study provides a reference for the design and performance analysis of trans-medium submersible configurations.

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.