Search Results (149)

The slow propagation speed of acoustic waves in water leads to significant variations and random fluctuations in communication delays among underwater acoustic sensor network (UASN) nodes. Conventional deep reinforcement learning (DRL)-based underwater acoustic network access methods can adaptively adjust their parameters and improve network communication efficiency by effectively utilizing inter-node delay differences for concurrent communication. However, they still suffer from shortcomings such as not accounting for random delay fluctuations in underwater acoustic links and low learning efficiency. This paper proposes a DRL-based delay-fluctuation-resistant underwater acoustic network access method. First, delay fluctuations are integrated into the state model of deep reinforcement learning, enabling the model to adapt to delay fluctuations during learning. Then, a double deep Q-network (DDQN) is introduced, and its structure is optimized to enhance learning and decision-making in complex environments. Simulations demonstrate that the proposed method achieves an average improvement of 29.3% and 15.5% in convergence speed compared to the other two DRL-based methods under varying delay fluctuations. Furthermore, the proposed method significantly enhances the normalized throughput compared to conventional Time Division Multiple Access (TDMA) and DOTS protocols. Full article

To address the multipath delay estimation problem in distributed hydrophone passive localization systems, a global energy minimization-based method is proposed in this paper. In this method, correlation pulses are treated as tracking targets, and their trajectories are estimated from correlograms formed by multiple frames. Specifically, an energy function is designed to jointly encode pulse similarity, motion continuity, trajectory persistence, data fidelity, and regularization, thereby reformulating multipath delay estimation as a global optimization problem. In order to balance the discreteness of observations and the continuity of trajectories, the optimization process is implemented alternating between discrete association (solved via $\alpha$-expansion) and continuous trajectory fitting (using weighted cubic splines). Furthermore, a dynamic hypothesis space expansion strategy based on trajectory merging and splitting is introduced to improve robustness while accelerating convergence. By exploiting both the intrinsic characteristics of correlation pulses in multi-frame processing and the physical properties of motion trajectories, the proposed method achieves higher tracking accuracy without requiring prior knowledge of the number of delay trajectories in a noisy environment. Numerical simulations under various noise conditions and sea trial results validate the superiorities of the proposed multipath delay estimation method. Full article

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume of acoustic equipment utilized by USVs. The generating mechanism of self-noise is clarified, and a self-noise propagation model is developed to examine its three-dimensional coupling properties within spatiotemporal fluctuation environments in the time-frequency-space domain. On this premise, the YOLOv11 object identification framework is innovatively applied to the delay-Doppler (DD) feature maps of self-noise, thereby overcoming the constraints of traditional time-frequency spectral approaches in recognizing noise with delay spread and overlapping characteristics. A comprehensive comparison with traditional models like YOLOv8 and SSD reveals that the suggested delay-Doppler YOLO (DD-YOLO) algorithm attains an average accuracy of 87.0% in noise source identification. An enhanced denoising method, termed optimized time-frequency regularized overlapping group shrinkage (OTFROGS), is introduced, using structural sparsity alongside non-convex regularization techniques. Comparative experiments with traditional denoising methods, such as the normalized least mean square (NLMS) algorithm, wavelet threshold denoising (WTD), and the original time-frequency regularized overlapping group shrinkage (TFROGS), reveal that OTFROGS outperforms them in mitigating USV self-noise. This study offers a dependable technological approach for optimizing the performance of USV acoustic systems and proposes a theoretical framework and methodology applicable to different underwater acoustic sensing contexts. Full article

This paper investigates the collaborative obstacle avoidance control of multiple autonomous underwater vehicles (AUVs) in underwater environments with communication delays and intermittent connectivity. Firstly, a novel time-delay piecewise differential inequality incorporating an exponential decay term is established, which systematically integrates state delay, intermittent control strategies, and event-triggered mechanisms. Secondly, the traditional request-response mechanism is replaced by a broadcast communication protocol, significantly reducing the requirement for continuous inter-AUV communication and enhancing overall communication efficiency. Thirdly, the obstacle avoidance problem of multiple AUVs is addressed through the implementation of a nominal-optimized controller. Obstacle avoidance safety conditions between unmanned vehicles and obstacles are derived by employing a zeroing control barrier function (CBF). Based on these safety conditions and input constraints, a quadratic programming (QP) framework is formulated to dynamically optimize control signals in real time, thereby ensuring the safe operation of the multi-AUV system. Finally, the efficacy of the proposed control method is validated through comprehensive simulation results, demonstrating its robustness and practical performance. Full article

Anthropogenic noise pollution is a significant global environmental issue that adversely affects the behavior, physiology, and auditory functions of aquatic species. However, studies on the effects of underwater noise on early developmental stages of fish remain scarce, particularly regarding the differential impacts of daytime versus nighttime noise exposure. In this study, zebrafish (Danio rerio) embryos were exposed to control group (no additional noise), daytime noise (100–1000 Hz, 130 dB, from 08:00 to 20:00) or nighttime noise (100–1000 Hz, 130 dB, from 20:00 to 08:00) for 5 days, and their embryonic development and oxidative stress levels were analyzed. Compared to the control group, the results indicated that exposure to both daytime and nighttime noise led to delays in embryo hatching time and a significant decrease in larval heart rate. Notably, exposure to nighttime noise significantly increased the larval deformity rate. Noise exposure, particularly at night, elevated the activities of catalase (CAT) and glutathione peroxidase (GPX), as well as the concentration of malondialdehyde (MDA), accompanied by upregulation of antioxidant-related gene expression levels. Nighttime noise exposure significantly increased the abnormality rate of otolith development in larvae and markedly downregulated the expression levels of otop1 related to otolith development regulation, while daytime noise exposure only induced a slight increase in the otolith abnormality rate. After noise exposure, the number of lateral neuromasts in larvae decreased slightly, yet genes (slc17a8 and capgb) related to hair cell development were significantly upregulated. Overall, this study demonstrates that both daytime and nighttime noise can induce oxidative stress and impair embryonic development of zebrafish, with nighttime noise causing more severe damage. Full article

This paper presents a behavior tree-based control architecture for end-to-end mission planning of an autonomous underwater vehicle (AUV) collaborating with a moving mothership in dynamic marine environments. The framework is organized into three phases—prepare and launch, execute the mission, and retrieval and docking—each encapsulated in an independent sub-tree to enable modular error handling and seamless phase transitions. The AUV and mothership operate entirely underwater, with real-time docking to a moving platform. An extended Kalman filter (EKF) fuses data from inertial, pressure, and acoustic sensors for accurate navigation and state estimation. At the same time, obstacle avoidance leverages forward-looking sonar (FLS)-based potential field methods to react to unpredictable underwater hazards. The system is implemented on the robot operating system (ROS) and validated in the Stonefish physics engine simulator. Simulation results demonstrate reliable mission execution, successful dynamic docking under communication delays and sensor noise, and robust retrieval from injected faults, confirming the validity and stability of the proposed architecture. Full article

Due to the dynamic and harsh underwater environment, which involves a long propagation delay, high bit error rate, and limited bandwidth, it is challenging to achieve reliable communication in underwater wireless sensor networks (UWSNs) and network support applications, like environmental monitoring and natural disaster prediction, which require energy efficiency and low latency. To tackle these challenges, we introduce AC-RL-based power control (ACRLPC), a novel hybrid MAC protocol that can efficiently integrate Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA)-based MAC and Time Division Multiple Access (TDMA) with Actor–Critic Reinforcement Learning (AC-RL). The proposed framework employs adaptive strategies, utilizing adaptive power control and intelligent access methods, which adjust to fluctuating conditions on the network. Harsh and dynamic underwater environment performance evaluations of the proposed scheme confirm a significant outperformance of ACRLPC compared to the current protocols of FDU-MAC, TCH-MAC, and UW-ALOHA-QM in all major performance measures, like energy consumption, throughput, accuracy, latency, and computational complexity. The ACRLPC is an ultra-energy-efficient protocol since it provides higher-grade power efficiency by maximizing the throughput and limiting the latency. Its overcoming of computational complexity makes it an approach that greatly relaxes the processing requirement, especially in the case of large, scalable underwater deployments. The unique hybrid architecture that is proposed effectively combines the best of both worlds, leveraging TDMA for reliable access, and the flexibility of CSMA/CA serves as a robust and holistic mechanism that meets the desired enablers of the system. Full article

The linear pseudo-measurements filter is adapted for use in a stochastic observation system with random time delays between the arrival of observations and the actual state of a moving object. The observation model is characterized by limited prior knowledge of the measurement errors distribution, specified only by its first two moments. Furthermore, the proposed model allows for a multiplicative dependence of errors on the state of the moving object. The filter incorporates direction angles and range measurements generated by several independent measurement complexes. As a practical application, the method is used for tracking an autonomous underwater vehicle moving toward a stationary target. The vehicle’s velocity is influenced by continuous random disturbances and periodic abrupt changes. Observations are performed by two stationary acoustic beacons. Full article

Whale signals originating in the vicinity of a triplet of underwater hydrophones, at a 2 km distance from each other, are recorded at the three sensors. They offer the opportunity to test simple models of propagation applied in the immediate neighborhood of the triplet, by comparing the arrival times and amplitudes of direct and reflected paths between the whale and the three hydrophones. Examples of recordings of individual fin whales passing by hydrophone triplets, based on the characteristics of their vocalizations around 20 Hz, are presented. Two types of calls are observed and their source wavelets extracted. Time segments are delimited around each call using a power detector. The time of arrival of the direct wave to the sensor and the Time Differences of Arrivals (TDOA) between sensors are obtained by correlation of the extracted source wavelets within the time segments. In addition to direct arrival, multiple reflections and the delays between the reflection and the direct arrival are automatically picked. A grid-search method of tracking the calls is presented based on the TDOA between three hydrophones and reflection delay times. Estimates of the depth of vocalization of the whale are made assuming a simple straight ray propagation model. The amplitude ratios between two hydrophones follow the spherical amplitude decay law of one over distance when the cetacean is in the immediate vicinity of the triplet, in a circle of radius 1.5 km sharing its center with the triplet’s center. Full article

The application of double-layer shell structure is very common in some situations that require complex loads and vibrations, such as key components such as the shell and wings of aerospace engines, and the shell of underwater vehicles. Many authors have conducted research on the vibration and acoustic radiation characteristics of double-layer cylindrical shells. By adding reinforcement and ribs between the double-layer cylindrical shells and optimizing structural design, passive vibration control techniques can effectively solve high frequency vibration problems, but the impact on mid to low frequency vibrations is still limited. Therefore, this article conducts theoretical research on a novel active vibration control method that inserts an active actuator between a double-layer cylindrical shell to achieve better mid low frequency vibration control effects. Firstly, the substructure admittance method is applied to analytically and dynamically model a double-layer cylindrical thin shell structure with active support, and the vibration power flow of the system is theoretically derived to evaluate the vibration reduction effect. Then, numerical simulation analysis was conducted on the influence of different configurations of six feedback control parameters, time delays, and other factors on the vibration power flow. Finally, based on the image, the conclusion is drawn that all six feedback control parameters can improve the vibration control effect of the coupled system to a certain extent, but not every feedback control parameter has a prominent effect, and the effective range of some parameters is relatively narrow. Full article

Biodiversity monitoring requires the discovery of multi-target tracking. The main requirement is not to reduce the localization error but the continuity of the tracks: a high ratio between the duration of the track and the lifetime of the target. To this end, we present an algorithm for detecting and tracking mobile underwater targets that utilizes reflections from active acoustic emission of broadband signals received by a rigid hydrophone array. The method overcomes the problem of a high false alarm rate by applying a tracking approach to the sequence of received reflections. A 2D time–distance matrix is created for the reflections received from each transmitted probe signal by performing delay and sum beamforming and pulse compression. The result is filtered by a 2D constant false alarm rate (CFAR) detector to identify reflection patterns that correspond to potential targets. Closely spaced signals for multiple probe transmissions are combined into blobs to avoid multiple detections of a single target. The position and velocity are estimated using the debiased converted measurement Kalman filter. The results are analyzed for simulated scenarios and for experiments in the Adriatic Sea, where six Global Positioning System (GPS)-tagged gilt-head seabream fish were released and tracked by a dedicated autonomous float system. Compared to four recent benchmark methods, the results show favorable tracking continuity and accuracy that is robust to the choice of detection threshold. Full article

Marine ship-radiated noise and multipath Doppler effect reduce the positioning accuracy of linear frequency modulation (LFM) signals in ocean waveguide environments. However, the assumption of Gaussian noise underlying most time–frequency domain algorithms limits their effectiveness in mitigating non-Gaussian interference. To address this issue, we propose a Deep-separable Conformer Wave-Unet (DC-WUnet)-based underwater acoustic signal enhancement network designed to reconstruct signals from interference and noise. The encoder incorporates the Conformer module and skip connections to enhance the network’s multiscale feature extraction capability. Meanwhile, the network introduces depthwise separable convolution to reduce the number of parameters and improve computational efficiency. The decoder applies a slope-based linear interpolation method for upsampling to avoid introducing high-frequency noise during decoding. Additionally, the loss function employs joint time–frequency domain constraints to prevent signal loss and compression, particularly under low Signal-to-Noise Ratio (SNR) conditions. Experimental evaluations under an SNR of −10 dB indicate that the proposed method achieves at least a 32% improvement in delay estimation accuracy and a 2.3 dB enhancement in output SNR relative to state-of-the-art baseline algorithms. Consistent performance advantages are also observed under varying SNR conditions, thereby validating the effectiveness of the proposed approach in shipborne noisy environments. Full article

In underwater signal processing, accurate time delay estimation (TDE) is of crucial importance for ensuring the reliability of data transmission. However, the complex propagation of sound waves and strong noise interference in the underwater environment make this task extremely challenging. Especially under the condition of low signal-to-noise ratio (SNR), the existing methods based on cross-correlation and deep learning struggle to meet requirements. Aiming at this core issue, this paper proposed an innovative solution. Firstly, a multi-sub-window reconstruction is performed on the frequency-sliding generalized colorboxpinkcross-correlation (FS-GCC) matrix between signals to capture the time delay characteristics from different frequency bands and conduct the enhancement and extraction of features. Then, the grayscale image corresponding to the generated FS-GCC matrix is used, and the multi-level noise features are extracted by the multi-layer convolution of denoising convolutional neural network (DnCNN), effectively suppressing the noise and improving the estimation accuracy. Finally, the model-agnostic meta-learning (MAML) framework is introduced. Through training tasks under various SNR conditions, the model is enabled to possess the ability to quickly adapt to new environments, and it can achieve the desired estimation accuracy even when the number of underwater training samples is limited. Simulation validation was conducted under the NOF and NCS underwater acoustic channels, and results demonstrate that our proposed approach exhibits lower estimation errors and greater stability compared with existing methods under the same conditions. This method enhances the practicality and robustness of the model in complex underwater environments, providing strong support for the efficient and stable operation of underwater sensor networks. Full article

Currently, it is becoming relevant to use cheap autonomous underwater vehicles (AUVs) to perform various underwater operations (environmental monitoring, aquatic protection, search and tracking of underwater biological objects, etc.). At the same time, the main way to reduce the cost of AUVs is to reduce the number of expensive on-board acoustic sensors. But this leads to a decrease in the accuracy of determining the parameters of the movement of these AUVs and the difficulty of performing missions. To solve this problem, this paper proposes a new method for the synthesis of an AUV navigation system, which recovers unavailable information (due to the absence of an expensive sensor) based on the dynamic model of the AUV and its thruster control signals. At the same time, these estimates are corrected using AUV position information, which is generated by an external hydroacoustic station (HAS) and transmitted via acoustic communication channels. This approach does not require synchronization procedures between the AUV and the HAS, but its accuracy significantly depends on the accuracy of determining the AUV dynamic model and the parameters of underwater currents. Two new approaches are proposed to ensure the accuracy of the navigation system. The first approach is to use the Kalman filter to combine data obtained from different sources with different periods and to take into account delays in receiving AUV position information at the stage of correcting estimates made by the Kalman filter. The second approach is to more accurately estimate the parameters of the AUV model and underwater currents based on data on the trajectory of the AUV obtained from the HAS. The use of these refined parameters of the AUV dynamic model makes it possible to significantly increase the accuracy of the navigation system. The simulation results carried out take into account the characteristics of real on-board sensors of the AUV and the HAS, and acoustic data transmission channels showed the high accuracy of the proposed method of constructing a navigation system, which reduces the cost of creating AUVs. In addition, the proposed algorithm can also be used during the failure of a number of AUV on-board navigation sensors. Full article

Ray-based blind deconvolution (RBD) is a technique for estimating the source-to-receiver array channel impulse response (CIR) without prior knowledge of the source waveform. Given its diverse applications, including source–receiver range estimation and the inversion of ocean waveguide parameters, RBD has been actively studied in underwater acoustics. However, the accuracy of CIR estimation in RBD may be compromised by phase uncertainty in the source waveform, necessitating enhancements in its performance. This paper proposes a method to improve RBD performance by estimating the phase of the source waveform using an optimization algorithm. Specifically, the particle swarm optimization (PSO) algorithm is employed to minimize phase estimation errors by optimizing the time delay for each receiver to maximize the beamformer output. The effectiveness of the proposed method was evaluated using two types of source signals: ship noise and linear frequency modulation (LFM), which corresponded to relatively low- and high-frequency sources, respectively. Performance comparisons with conventional RBD across various source-to-vertical line array distances revealed that the proposed method yielded more compact arrival paths with reduced time spread and a higher signal-to-noise ratio at short distances in the low-frequency band, and it consistently outperformed conventional RBD at all distances in the high-frequency band. Full article