Hybrid underwater gliders (HUGs) combine buoyancy-driven gliding with propeller-assisted propulsion, offering extended endurance and enhanced mobility for complex underwater missions. However, precise depth control remains challenging due to system uncertainties, environmental disturbances, and inadequate adaptability of conventional control methods. This study proposes a novel optimized line-of-sight active disturbance rejection control (OLOS-ADRC) strategy for HUG depth tracking in the vertical plane. First, an Optimized Line-of-Sight (OLOS) guidance dynamically adjusts the look-ahead distance based on real-time cross-track error and velocity, mitigating error accumulation during path following. Second, a Tangent Sigmoid-based Tracking Differentiator (TSTD) enhances the disturbance estimation capability of the Extended State Observer (ESO) within the Active Disturbance Rejection Control (ADRC) framework, improving robustness against unmodeled dynamics and ocean currents. As a critical step before costly sea trials, this study establishes a high-fidelity simulation environment to validate the proposed method. The comparative experiments under gliding and hybrid propulsion modes demonstrated that OLOS-ADRC has significant advantages: the root mean square error (RMSE) for depth tracking was reduced by 83% compared to traditional ADRC, the root mean square error for pitch angle was decreased by 32%, and the stabilization time was shortened by 14%. This method effectively handles ocean current interference through real-time disturbance compensation, providing a reliable solution for high-precision HUG motion control. The simulation results provide a convincing foundation for future field validation in oceanic environments. Despite these improvements, the study is limited to vertical plane control and simulations; future work will involve full ocean trials and 3D path tracking.

The accuracy and efficiency of the equivalent source method (ESM) for underwater radiated-field prediction depend strongly on the position and number of the equivalent sources. The conventional uniform distribution (UD) is fixed before the field is observed, and the coherence-based optimum configuration (COC) ranks candidates by local pairwise correlations, blind to the singular directions the field excites. We propose the leverage-based effective-subspace configuration (LESC): it screens the singular directions the measured pressure excites, ranks candidates by leverage scores within that subspace, and prunes redundant sources by a coherence threshold, solving the strengths by cross-validated Tikhonov regularization. In free-space simulations of a baffled circular piston and a dual-hotspot planar source, LESC uses fewer, position-adaptive sources while cutting the 5000 Hz error by 73–$83\%$ compared with UD and by 73–$74\%$ against COC, and maintains an error of $2.33\%$ at 6300 Hz, where UD and COC degrade sharply, at one-third to one-half of COC’s configuration cost. An ablation identifies the effective singular-direction screening as the decisive ingredient. The ordering is reproduced at matched source counts and under colored and spatially correlated noise, reversing only where the source count is small relative to the excited-direction count. The study is confined to free-space radiation from planar sources, within which these advantages are established.

Underwater glider (UG) formations exhibit significant trajectory deviations when subjected to non-uniform ocean currents, complicating spatial reconfiguration upon exiting complex marine environments and compromising environmental monitoring fidelity. To solve these problems, we develop a rapid formation reconfiguration algorithm based on distributed model predictive control (DMPC). The algorithm combines a flexible boundary-triggering mechanism for autonomous control suspension and rapid activation, a nonlinear vector heading pre-compensation scheme to counteract strong current disturbances, and a multi-objective leader capability assessment with a dynamic rotation strategy for optimized leader selection. The predictive plant model is calibrated utilizing empirical sea trial datasets, and its control performance is validated through comparative simulations against conventional MPC and active disturbance rejection control (ADRC). The results demonstrate that the proposed algorithm reduces the standard deviation of lateral inter-glider distance by 42% and 17% relative to ADRC and conventional MPC, shortens the reconfiguration time to 13.3 h, and lowers the maximum relative deflection, lateral distance standard deviation and average energy consumption. This framework significantly improves formation stability, reconfiguration efficiency, and energy efficiency, providing a robust methodology for persistent swarm deployments.

Reliable ship-radiated-noise recognition depends on both model design and evaluation protocol. On DeepShip, the common recording-level split still places training-seen ship identities in the test set; we define this overlap as a contamination rate of $68.1\%$. A ship-level protocol reduces contamination to 0 under our metadata-based identity grouping, and a controlled comparison with the same architecture and hyperparameters shows a $7.11$-percentage-point (pp) accuracy difference. We also refine the constant-Q harmonic coefficient (CQHC) by formulating envelope–harmonic separation as non-negative constrained least squares and replacing the original one-step approximation with non-negative harmonic projection and regularized closed-form back-substitution. The three inputs, whose vertical axes represent linear frequency, Mel bands, and harmonic order, are encoded separately and fused at an intermediate level by soft mixture-of-experts gating. Bayesian hybrid attention provides posterior-based predictive uncertainty. Under the DeepShip recording-level protocol, accuracy is $88.64\%$, exceeding input stacking and decision averaging by $3.83$ and $2.50$ pp. The expected calibration error decreases from $0.0724$ to $0.0413$, and rejection at $90\%$ coverage reduces the error by $27.0\%$. At the recording level, confidence-weighted voting reaches $94.17\%$ over 120 test recordings. Without dataset-specific tuning, ShipsEar accuracy is $98.63\%$ with a segment-random split and $95.05\%$ with recording-disjoint evaluation.

Hybrid Underwater Glider (HUG) serve as critical platforms for extended and large-scale ocean observations. However, achieving precise trajectory tracking in complex and dynamic marine environment, remains a significant challenge. This paper addresses the multimodal trajectory tracking problem for HUG by proposing a composite control framework integrating Active Disturbance Rejection Control (ADRC), Model Predictive Control (MPC), and Adaptive Line-of-Sight (ALOS). First, a six-degree-of-freedom dynamic model is established, ALOS is designed to achieve decoupling and smooth guidance of horizontal and vertical motions by dynamically adjusting the look-ahead distance and depth-pitch coupling mechanism. Second, an Extended State Observer (ESO) was validated for real-time estimation of total system disturbances. MPC replaced the conventional Nonlinear Law State Error Feedback (NLSEF), utilizing rolling optimization strategies to address actuator physical constraints and state constraints, thereby enhancing the predictive capability and robustness of the controller. Finally, the framework’s performance was validated through multimodal task simulations. Simulation results demonstrate that this framework significantly outperforms traditional methods. During trajectory tracking, horizontal tracking error is maintained within 3 meters, depth deviation is reduced to 0.52 meters. In the constant-depth navigation phase, depth fluctuations are less than 0.15 meters. This approach effectively addresses the challenge of synergistic control under model uncertainty and multi-mode switching.

Small underwater vehicles are widely used in reconnaissance, infiltration, and special underwater missions due to their compact size, low noise signature, and high maneuverability. However, during typical operating conditions, vibrations induced by internal components such as electric motors and propulsion systems can be transmitted through the structure and radiate acoustic noise into the surrounding water, which compromises the vehicle’s acoustic stealth and increases the risk of detection by passive sonar. In this study, a small frogman carrier is taken as the research object, and a coupled acoustic–structure interaction (ASI) finite element model is developed using COMSOL Multiphysics. The model simulates the vibroacoustic behavior of the carrier under typical excitation conditions to predict its radiated noise characteristics. Through the analysis of structure-borne vibration responses and sound pressure field distributions, the frequency spectrum, spatial directivity, and dominant source mechanisms of the radiated noise are investigated. The results provide theoretical insights and design references for low-noise design, detection risk assessment, and acoustic stealth optimization of small underwater vehicles.