Search Results (6,504)

As ocean resources are further developed and utilized, bionic covert underwater acoustic communication (CUAC) is increasingly important for military and underwater telemetry applications. The primary purpose of this study was to design a highly secure and undetectable text information (TI) encryption mechanism to realize CUAC using real bottlenose dolphin vocalizations (BDVs). For this purpose, a chaotic encryption scheme, spread spectrum (SS) technology, and a modified chaotic Hénon map (MCHM) were integrated into a TI encryption and hiding (EH) mechanism. Four BDVs and four test TIs were employed to demonstrate the performance of the proposed MCHM-based TI EH mechanism (MCHMTIEHM). The simulation results show that the MCHMTIEHM yields more accurate de-hiding and decryption results. When the correct encryption and decryption parameters were used, the test TI was completely recovered and could be recognized by humans. When the MCHM encryption and decryption parameters SP_x and n_I were not identical, tests involving TI01, TI02, TI03, and TI04 demonstrated correct de-hiding and error decryption performance; in particular, the test TI had superior correct de-hiding and error decryption results, was unrecoverable, and could not be recognized by the human eye. The modified amplitude correlation coefficient (ACC) and modified unified average amplitude change intensity (UACI) metrics were used to evaluate the hiding performance of MCHM-based encryption of TI using BDVs. The simulation results show that the average modified ACC and average UACI were 0.99995924 and 3.84 × 10⁻⁶, respectively. Performance was evaluated in terms of the average number of changing SS bit rates (NCSSBRs), the average number of changing bit rates (NCBRs), and the average number of changing character rates (NCCRs) for correct de-hiding and correct/erroneous TI decryption. The average NCSSBRs, NCBRs, and NCCRs were all 0% in correct de-hiding and encryption scenarios, while they were 49.29%, 47.65%, and 98.10%, respectively. with correct de-hiding and error-encryption scenarios. In summary, the proposed MCHMTIEHM yields superior encryption and hiding performance. Full article

This study focuses on how mesoscale eddies at the Kuroshio boundary in the East China Sea modulate underwater acoustic propagation. Using high-resolution reanalysis data from the Hybrid Coordinate Ocean Model (HYCOM) and validated acoustic ray-tracing simulations, the OW + SLA method is employed for eddy identification and classification. Statistical analysis of 120 eddy events from 2015 to 2020 clarifies their seasonal variation characteristics. Warm eddies shift the convergence zone 15–30 km away from the sound source and broaden it by 20–40%, while cold eddies shift it 10–25 km toward the source and narrow it by 15–35%. A linear relationship exists between eddy amplitude and acoustic transmission loss (TL = 72.4 + 0.42 h, R² = 0.61), where TL is the transmission loss in decibels (dB) and h is the eddy amplitude in meters (m), and there are depth-dependent transmission loss modulation effects. These results provide practical guidance not only for sonar system design and acoustic communication optimization but also for error correction in underwater acoustic navigation systems operating in eddy-prone environments. Full article

MEMS hydrophones, as critical sensors for maritime security and underwater information acquisition, have sensitive membrane structures that exhibit insufficient ability to withstand hydrostatic pressure, necessitating an overload-protection design. Based on buckling stability theory, a collaborative optimization method for overload-protection column design was proposed, integrating theoretical analysis, finite-element simulation, and process feasibility. An optimized design scheme for hydrophone overload-protection columns was established by comprehensively considering geometric buckling-resistant design, micro-gap anti-adhesion requirements, minimal impact on sensitivity, and micro/nano-fabrication constraints. The results indicate that intermediate slenderness columns with radii between 5.5 μm and 7.5 μm sufficiently meet both fabrication and operational requirements, effectively providing overload protection. Furthermore, at water depths not exceeding 382 m, the MEMS hydrophone can maintain the integrity of its membrane structure without column buckling. Full article

A hybrid aquatic–aerial vehicle (HAAV) is a novel type of aircraft capable of both aerial flight and underwater navigation. Inspired by the swan’s gliding and landing motion on water surfaces, this study investigates the dynamic modeling and integrated optimization design of an HAAV equipped with a biomimetic skipping plate. By comprehensively accounting for the aerodynamic, impact, hydrodynamic, and frictional forces during the water entry process, a dynamic model for the HAAV’s gliding water entry is established. The reliability of the model is verified through comparisons between numerical simulations and theoretical predictions. Parametric modeling of the skipping plate’s configuration and layout is performed to analyze the influence of different parameters on the water entry dynamics. With the objectives of minimizing the overload and pitch angle variation, a hybrid infilling strategy based on a radial basis function neural network (RBFNN) surrogate model is constructed to improve optimization efficiency. This is combined with a quantum-behaved particle swarm optimization (QPSO) algorithm to conduct the multi-objective optimization of the biomimetic plate, thereby obtaining its optimal configuration and layout parameters. The results demonstrate that the established dynamic model is effective and can accurately capture the kinematic characteristics of the gliding water entry process. The error between the peak load and the pitch angle variation is less than 5%. Compared with the direct QPSO algorithm, the proposed method reduces the number of model evaluations by 66.7%, the computational time by 52.1%, and the optimal solution response value by 12.01%, demonstrating strong potential for engineering applications. Full article

In this paper, we propose a high-sensitivity fiber Bragg grating (FBG) pressure sensor based on an X-shaped mechanical transducer that converts external pressure into predominantly axial strain, thereby helping to alleviate bending-dominant spectral distortion and improve measurement stability. A theoretical model is developed to describe the relationship between applied force, pressure, and grating wavelength shift. Experimental optimization was conducted by varying Ethylene Propylene Diene Monomer (EPDM) thickness, bonding materials, and contact area to achieve sensitivities of 0.291 nm/N, 0.409 nm/N, and 0.462 nm/N, respectively, within the investigated force range of 0–10 N. For measuring the under water pressure, the sensor exhibits a high sensitivity of 0.596 nm/kPa within the investigated pressure range of 0–6 kPa. The results demonstrate the nice sensing performance with high sensitivity, good linearity, and excellent repeatability. This work provides an effective approach for high-performance FBG-based pressure sensing in underwater and harsh environments. Full article

The mechanism by which fish enhance hydrodynamic performance through collective swimming is a research hotspot in the field of underwater bionic robots. This study employs the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) to conduct numerical simulations on a two-dimensional, single-degree-of-freedom (1-DOF) autonomous propulsion bionic fish swarm. It systematically investigates the effects of swarm size and inter-individual spacing on swimming speed and cost of transport (CoT) under two typical configurations: series and parallel arrangements. Findings reveal that hydrodynamic benefits are highly dependent on the spatiotemporal evolution of flow field structures. In the series configuration, an optimal spacing range of 1.5 L to 2.0 L exists within the school, where the “wake capture” effect is pronounced. Trailing fish achieve a maximum speed increase of approximately 41.1% while significantly reducing energy consumption. However, as spacing increases to 2.5 L, the cooperative gain for front and middle-row individuals rapidly diminishes, and the lead fish even experiences significant performance loss. Uniquely, the trailing fish in the four-fish formation exhibits distinct flow field reorganization and performance recovery at the 4.5 L trailing position. In the parallel formation, the “channel effect” and “blocking effect” of the fluid dominate. The study identifies 0.4 L laterally as the critical instability spacing under the investigated kinematic regime, where strong destructive interference causes a sharp deterioration in individual swimming performance. Additionally, the parallel formation exhibits pronounced positional differentiation. Central individuals, constrained by dual lateral flow fields, experience restricted lateral wake expansion and accelerated energy dissipation, resulting in significantly weaker escape capabilities from low-speed conditions compared to marginal individuals. The vortex-dynamic mechanism revealed herein provides theoretical foundations for formation control in multi-fish biomimetic cooperative systems. Full article

To meet the future demands of high-rate transmission and full-coverage networks, radio frequency–underwater wireless optical communication (RF-UWOC) relaying systems are considered a promising heterogeneous communication architecture. The rate-splitting (RS) scheme, through its power allocation (PA) mechanism, provides a generalized framework for the performance evaluation of such systems. Based on this, this paper analyzes the performance of an RS-based RF-UWOC system under hardware impairments (HIs) and interference. Analytical expressions of the outage probability (OP) and ergodic capacity (EC) for the considered system are formulated within a generalized framework, which encompasses the conventional RF-UWOC system as a special case. The results indicate that the OP and EC are affected by HIs, interference transmit power, the PA coefficients, channel fading, pointing errors (PEs), and detection types of the UWOC link. Furthermore, the asymptotic results for the OP and the diversity gain (DG) are explicitly characterized. For a fixed interference transmit power, the DG is mainly dominated by the channel fading severity, PEs effect, and the detection scheme. When the interference transmit power is comparable to the desired signal power, the system operates in an interference-limited regime, and the DG decreases to zero. It is also revealed that HIs and PA coefficients affect the coding gain but not the DG. Moreover, the existence of an optimal PA scheme improves the reliability of the RS-based system. Full article

Time-series machine learning models represented by long short-term memory (LSTM) networks provide an effective way to obtain high-precision sound speed profiles (SSPs) quickly and at low cost, which can meet the practical application requirements of underwater sonar systems. However, in sea areas with frequent strong internal solitary waves, the large-amplitude sound speed anomalies caused by them will seriously interfere with model learning in the form of strong outlier features, resulting in a sharp drop in SSP prediction accuracy and significant degradation of the generalization stability and robustness of the model. To address this problem, this paper proposes a time-series SSP prediction method based on a bidirectional long short-term memory (Bi-LSTM) network. First, Empirical Orthogonal Function (EOF) decomposition is used to realize the low-dimensional feature representation of SSPs, and then the bidirectional time-series feature capture capability of Bi-LSTM is used to predict the SSP sequence with large disturbances caused by strong internal solitary waves. Multiple groups of comparative experiments based on the measured temperature chain data in the continental slope area of the South China Sea show that the Bi-LSTM model has a significant improvement in prediction accuracy and robustness compared with the classical LSTM model. Among them, the Bi-LSTM model with EOF decomposition achieves a correlation coefficient of 0.995 and an average Root Mean Square Error (RMSE) as low as 0.387 m/s. Under the condition of internal solitary wave disturbance, the classical LSTM is difficult to effectively capture the large abrupt change in sound speed, while the proposed Bi-LSTM model can still achieve accurate prediction of the SSP in the disturbance section, and has both the feature recognition and evolution prediction capabilities for the strongly nonlinear internal solitary wave process. This method provides effective technical support for the rapid and large-scale reconstruction of the sound speed field under the disturbance of strong internal solitary waves. Full article

In recent years, as underwater vehicles continue to improve their noise reduction capabilities, sonar-based detection has faced significant challenges, and non-acoustic detection has become a research focus. Gravity gradient detection, owing to its excellent concealment and anti-interference capability, is regarded as an important non-acoustic means for underwater target detection. Based on the structural characteristics of an underwater vehicle, this paper establishes a homogeneous ellipsoidal hull (HEH) model composed of two similar rotating ellipsoids. This model assumes that the mass of an underwater vehicle is completely uniformly distributed over the outer hull. Analytical formulas for the gravity anomaly and gravity gradient anomaly generated by this model are derived, and their spatial distribution characteristics are analyzed. Furthermore, based on the HEH model, the feasibility underwater vehicle detection using the vertical gravity gradient component is analyzed. Results show that when the accuracy of the gravity gradiometer reaches 10⁻⁴ E, the detection distance for a large underwater vehicle with a displacement of 18,750 t can reach 570 m. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article

To facilitate the practical deployment and engineering implementation of multi-robot coordination for biomimetic underwater spherical robots (BUSRs), it is imperative to develop a formation tracking control method with a simple structure, a small number of tunable parameters, convenient parameter tuning and strong anti-disturbance capability. This study proposes a formation controller integrating virtual structure (VS), consensus protocol, and parallel output-velocity-type active disturbance rejection control (POV-ADRC), denoted as VS-C-POV-ADRC. A rotating global (RG) coordinate system is established to decouple robot positions from heading angles, which makes the parameter tuning more convenient. A double-loop control architecture is constructed, where the outer consensus control loop generates the desired velocity for each robot based on virtual-structure reference positions, and the inner POV-ADRC loop achieves high-precision velocity tracking. The proposed controller features a compact structure with only five adjustable parameters per motion direction, realizing easy engineering implementation and adaptation to the limited computing capacity of BUSRs. The simulation and experiment results demonstrate that the proposed algorithm enables robots to maintain a stable formation and achieve trajectory tracking accuracy within one body length, while exhibiting superior disturbance rejection. The proposed method provides a feasible and practical solution for BUSR formation control. Full article

Underwater radiated noise (URN) has attracted increasing attention due to its environmental impact, with cavitation recognized as the dominant source. This study investigates cavitation-generation mechanisms and associated noise radiation for open-type and gap-type wings using high-fidelity numerical simulations. Cavitation noise was predicted using the Ffowcs Williams–Hawkings (FW–H) equation. The Fitzpatrick–Strasberg bubble noise model was independently employed for analysis to relate cavitation dynamics and cavity-volume variation to the resulting acoustic emissions. The results show that the gap-type configuration produces significantly stronger low-frequency noise, with the Tip Leakage Vortex Cavitation (TLVC) contributing up to 15 dB/Hz higher noise levels than the Tip Separation Vortex Cavitation (TSVC). This enhancement is attributed to the strong interaction between TLVC and TSVC, which amplifies cavitation dynamics and acoustic emissions. Analysis of three gap sizes reveals that, for small gaps, this interaction induces periodic cavitation behavior, generating a distinct harmonic component at St ≈ 2. As the gap size increases, the TLVC-TSVC interaction weakens, and the cavitation behavior transitions toward that of the open-type configuration, leading to the disappearance of the tonal component. These findings highlight the critical role of gap-induced vortex interaction in determining URN characteristics. Full article

The Mediterranean Sea is a major biodiversity hotspot increasingly affected by biological invasions, climate warming, and habitat degradation. Among the most successful invaders are the rabbitfish species Siganus luridus and Siganus rivulatus, Lessepsian migrants from the Red Sea that are now widespread across the eastern Mediterranean. This study examined how these invasive herbivores influence native herbivore assemblages in shallow coastal habitats around Lipsi Island in the Aegean Sea, Greece. Using Underwater Visual Census (UVC) surveys and in situ feeding observations, we quantified the abundance and grazing activity of invasive rabbitfish relative to that of the native herbivores Sparisoma cretense and Sarpa salpa. Invasive rabbitfish represented approximately 35% of the herbivore assemblages and showed clear habitat and dietary preferences. Significant negative correlations were observed between invasive foraging activity and the feeding rate of the native S. cretense, while no such effect was found for S. salpa. High habitat overlap between S. luridus and S. cretense suggests that this native species may be more susceptible to competition on rocky substrates. Evidence of partial resource partitioning was observed, including increased use of seagrass habitats by S. salpa. These findings highlight how invasive herbivores can restructure native herbivore communities and alter grazing dynamics in eastern Aegean coastal ecosystems. Given the ongoing sea warming and widespread decline of seagrass habitats across the Mediterranean, understanding these competitive interactions is therefore essential for assessing future biodiversity trajectories and informing management strategies. Full article

This paper considers the problem of controlling the motion of an autonomous underwater vehicle (AUV) following a leader in a leader–follower scheme based on visual information about the leader’s position. It is assumed that the leader is equipped with a system of light markers with known geometry, and the follower determines its relative position based on data from an onboard camera without using a hydroacoustic communication channel or direct exchange of navigation information. To synthesize the control law, a reinforcement learning method based on the Proximal Policy Optimization algorithm is used. Policy learning is performed in a simulation environment, taking into account the dynamic model of the agent in the horizontal plane and observation noise. A structure of state space, actions, and reward function is proposed, aimed at minimizing the error in relative position and orientation. Additionally, Bayesian optimization of the weight coefficients of the reward function is performed. Bayesian optimization of the reward function weights reduces the RMS tracking error from 0.24 m to 0.09 m and demonstrates that heading regulation has a significantly stronger impact on stability than position penalties. The results of modeling, testing in the Webots environment, and experiments on MiddleAUV class devices confirm the feasibility and scalability of the approach. It is shown that a single trained policy ensures stable formation maintenance when the number of follower agents and initial conditions change without additional retraining. Full article

Autonomous obstacle avoidance is a critical capability for Autonomous Underwater Vehicles (AUVs) to operate safely in dynamic and uncertain marine environments. Traditional AUV control methods rely on precise physical modeling and preset rules, yet they struggle to adapt to multiple sources of uncertainty, such as random initial states, dynamic obstacles, and varying currents. In recent years, deep reinforcement learning has provided a new avenue for data-driven adaptive policy learning. However, it remains insufficient for handling long-horizon tasks with sparse rewards. While hierarchical reinforcement learning can mitigate reward sparsity through temporal abstraction, it often faces challenges including exploration–exploitation imbalance, slow global convergence, and insufficient safety guarantees. Furthermore, most existing studies neglect dynamic environmental disturbances and task continuity, which further limits the practical application of these algorithms. To address these challenges, this paper proposes a hierarchical curiosity-driven AUV obstacle avoidance algorithm (HDAO), designed for autonomous obstacle avoidance in dynamic and uncertain underwater environments. The core design of HDAO incorporates several key innovations. Firstly, it introduces a Collision Threat Index for dynamic obstacles, which enables explicit risk perception and quantifies collision threats, thereby enhancing the policy’s generalization and robustness. Secondly, a task-decoupled hierarchical architecture is employed to synergistically optimize global path planning and local obstacle avoidance behaviors. This approach effectively manages long-horizon navigation tasks while alleviating high-dimensional training pressure. Finally, a novel reward mechanism is designed by integrating hierarchical active exploration with curiosity-driven passive exploration. This mechanism effectively incentivizes the agent to explore unvisited areas under sparse reward conditions and dynamically balances exploration and exploitation. Experimental results demonstrate that HDAO significantly outperforms existing methods in terms of obstacle avoidance success rate, training convergence speed and robustness against external disturbances. Full article