Search Results (268)

An acoustic Doppler current profiler (ADCP) installed on a platform produces rotational tangential velocity as a result of variations in the platform’s attitude, with both the tangential velocity and radial orientation varying between each pulse’s transmission and reception by the transducer. These factors introduce errors into the measurements of vessel velocity and flow velocity. In this study, we address the errors induced by dynamic factors related to variations in attitude and propose an ADCP attitude dynamic error correction method based on angular velocity tensor and radius vector estimation. This method utilizes a low-sampling-rate inclinometer and compass data and estimates the angular velocity tensor based on a physical model of vessel motion combined with nonlinear least-squares estimation. The angular velocity tensor is then used to estimate the transducers’ radius vectors. Finally, the radius vectors are employed to correct the instantaneous tangential velocity within the measured velocities of the vessel and flow. To verify the effectiveness of the proposed method, field tests were conducted in a water pool. The results demonstrate that the proposed method surpasses the attitude static correction approach. In comparison with the ASC method, the average relative error in vessel velocity during free-swaying movement decreased by 20.94%, while the relative standard deviation of the error was reduced by 17.38%. Full article

To enhance the performance of entropy algorithms in analyzing complex time series, generalized Gaussian distribution improved permutation entropy (GGDIPE) and its multiscale variant (MGGDIPE) are proposed in this paper. First, the generalized Gaussian distribution cumulative distribution function is employed for data normalization to enhance the algorithm’s applicability across time series with diverse distributions. The algorithm further processes the normalized data using improved permutation entropy, which maintains both the absolute magnitude and temporal correlations of the signals, overcoming the equal value issue found in traditional permutation entropy (PE). Simulation results indicate that GGDIPE is less sensitive to parameter variations, exhibits strong noise resistance, accurately reveals the dynamic behavior of chaotic systems, and operates significantly faster than PE. Real-world data analysis shows that MGGDIPE provides markedly better separability for RR interval signals, EEG signals, bearing fault signals, and underwater acoustic signals compared to multiscale PE (MPE) and multiscale dispersion entropy (MDE). Notably, in underwater target recognition tasks, MGGDIPE achieves a classification accuracy of 97.5% across four types of acoustic signals, substantially surpassing the performance of MDE (70.5%) and MPE (62.5%). Thus, the proposed method demonstrates exceptional capability in processing complex time series. Full article

Water surface micro-amplitude waves (WSMWs) of identical frequency are elicited as acoustic waves propagating through water. This displacement can be translated into an intermediate frequency (IF) phase shift through transmitting a frequency modulated continuous wave (FMCW) towards the water surface by a millimeter-wave radar, and information transmission across the water–air interface is achieved via the signal reconstruction method. In this paper, a novel mathematical model based on energy conversion from underwater acoustic to vibration (ECUAV) is presented. This method was able to obtain WSMW vibration information directly by measuring the sound source level (SL). An acoustic electromagnetic wave-based information transmission (AEIT) system was integrated within the water tank environment. The measured distribution of SL within the frequency range of 100 Hz to 300 Hz exhibited the same amplitude variation trend as predicted by the ECUAV model. Thus, the WSMW formation process at 135 Hz was simulated, and the phase information was extracted. The initial vibration information was retrieved through a combination of phase unwinding and Butterworth digital filtering. Fourier transform was applied to the vibrational data to accurately reproduce the acoustic frequency of underwater nodes. Finally, the dual-band binary frequency shift keying (BFSK) modulated underwater encoding acoustic signal was effectively recognized and reconstructed by the AEIT system. Full article

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, a DOA tracking algorithm based on a novel beam-domain signal processing technique is proposed to ensure robust DOA tracking of an interested underwater target under a low signal-to-noise ratio (SNR) environment. Firstly, the beam-based observation is designed and proposed, which innovatively applies beamforming after array-based observation to achieve specific spatial directivity. Next, the proportional–integral–differential (PID)-optimized Olen–Campton beamforming method (PIDBF) is designed and proposed in the beamforming process to achieve faster and more stable sidelobe control performance to enhance the SNR of the target. The adaptive dynamic beam window is designed and proposed to focusing the observation on more likely observation area. Then, by utilizing the extended Kalman filter (EKF) tracking framework, a novel PIDBF-optimized beam-domain DOA tracking algorithm (PIDBF-EKF) is proposed. Finally, simulations with different SNR scenarios and comprehensive analyses are made to verify the superior performance of the proposed DOA tracking approach. Full article

Nonlinear acoustic signals, specifically the parametric effect, offer significant advantages over linear signals because the low frequencies generated in the medium due to the intermodulation of the emitted frequencies are highly directional and can propagate over long distances. Due to these characteristics, a detailed analysis of these signals is necessary to accurately estimate the Time of Arrival (ToA) and amplitude parameters. This is crucial for various communication applications, such as sonar and underwater location systems. The research addresses a notable gap in the literature regarding comparative methods for analyzing nonlinear acoustic signals, particularly focusing on ToA estimation and amplitude parameterization. Two types of nonlinear modulations are examined: parametric Frequency-Shift Keying (FSK) and parametric sine-sweep modulation, which correspond to narrowband and broadband signals, respectively. The first study evaluates three ToA estimation methods—threshold, power variation (Pvar), and cross-correlation methods for the modulations in question. Following ToA estimation, the amplitude of the received signals is analyzed using acoustic signal processing techniques such as time-domain, frequency-domain, and cross-correlation methods. The practical application is validated through controlled laboratory experiments, which confirm the robustness and effectiveness of the existing methods proposed under study for nonlinear (parametric) acoustic signals. Full article

Due to the physical characteristics of acoustic channels, the performance of underwater acoustic communication networks (UACNs) is more susceptible to the impacts of multipath and Doppler effects. Channel quality can serve as a measure of the reliability of underwater communication links. A cross-layer routing protocol based on channel quality (CLCQ) is proposed to improve the overall network performance and resource utilization. First, the BELLHOP ray model is used to calculate the channel impulse response combined with the winter sound speed profile data of a specific sea area. Then, the channel impulse response is integrated into the communication system to evaluate the channel quality between nodes based on the bit error rate (BER). Finally, during the selection of the next hop node, a reinforcement learning algorithm is employed to facilitate cross-layer interaction within the protocol stack. The optimal relay node is determined by the channel quality index (BER) from the physical layer, the buffer state from the data link layer, and the node residual energy. To enhance the algorithm’s convergence speed, a forwarding candidate set selection method is proposed which takes into account node depth, residual energy, and buffer state. Simulation results show that the packet delivery rate (PDR) of the CLCQ is significantly higher than that of Q-Learning-Based Energy-Efficient and Lifetime-Extended Adaptive Routing (QELAR) and Geographic and Opportunistic Routing (GEDAR). Full article

The Acoustic Doppler Current Profiler (ADCP) on a platform generates rotational linear velocity due to dynamic factors in attitude changes, leading to measurement errors in vessel and water flow velocities. This study derives and analyzes these errors, focusing on factors such as emission angle, transducer position, water depth, and measured depth, while also accounting for the variation in linear velocity and radial direction during each transmit–receive pulse cycle in the simulations. A method is proposed that introduces the concept of an equivalent radial radius to correct vessel and flow velocities, specifically designed for the common scenario where the ADCP is installed on the central longitudinal section of a vessel undergoing free roll motion. This method is suited for shallow water conditions without waves, with measurements taken vertically downward. It uses least squares fitting with an exponentially decaying sinusoidal model to process low-sampling-rate inclinometer data from the ADCP. This approach requires only the processing of measured data based on existing ADCP hardware, without the need for additional equipment. Field tests in a pool demonstrate that the proposed method significantly reduces vessel velocity errors, outperforming the traditional attitude static correction method. Full article

In the realm of underwater active target detection, the presence of reverberation is an important factor that significantly impacts the efficacy of detection. This article introduces the improved permutation entropy algorithm into the analysis of active underwater acoustic signals. Based on the significant difference between the improved permutation entropy in the frequency domain and the time domain, a frequency-domain-improved permutation entropy detection algorithm is proposed. The performance of this algorithm and the energy detection algorithm are compared and analyzed under the same conditions. The results show that the spectral entropy detector is about 2.7 dB better than the energy detector, realized via active small target signal detection under a reverberation background. At the same time, based on the characteristics of improved permutation entropy changing with the length of processed data, the short-time Fourier transform is integrated into frequency domain entropy detection to obtain distance and velocity information of the target. To validate the proposed methods, comparative analysis experiments were executed utilizing actual experiment data. The outcomes of both simulation and actual experiment data processing demonstrated that the sliding entropy feature detection method for signal spectrum has a small computational complexity and can quickly determine whether there is a target echo in the receive data. The two-dimensional entropy feature detection method for short-time signal spectra was found to effectively mitigate the impact of reverberation intensity and while enhancing the prominence of the target signal, thereby yielding a more robust detection outcome. Full article

Underwater acoustic target recognition (UATR) is crucial for defense and ocean environment monitoring. Although traditional methods and deep learning approaches based on time–frequency domain features have achieved high recognition rates in certain tasks, they rely on manually designed feature extraction processes, leading to information loss and limited adaptability to environmental changes. To overcome these limitations, we proposed a novel end-to-end underwater acoustic target recognition model, 1DCTN. This model directly used raw time-domain signals as input, leveraging one-dimensional convolutional neural networks (1D CNNs) to extract local features and combining them with Transformers to capture global dependencies. Our model simplified the recognition process by eliminating the need for complex feature engineering and effectively addressed the limitations of LSTM in handling long-term dependencies. Experimental results on the publicly available ShipsEar dataset demonstrated that 1DCTN achieves a remarkable accuracy of 96.84%, setting a new benchmark for end-to-end models on this dataset. Additionally, 1DCTN stood out among lightweight models, achieving the highest recognition rate, making it a promising direction for future research in underwater acoustic recognition. Full article

Underwater wireless optical communication (UWOC) has attracted increasing attention due to its advantages in bandwidth, latency, interference resistance, and security. Photodetectors, as a crucial part of receivers, have been continuously developed with the great progress that has been made in advanced materials. Metal halide perovskites emerging as promising optoelectronic materials in the past decade have been used to fabricate various high-performance photodetectors. In this work, high-performance CsPbBr₃ perovskite PDs were realized via solution process, with low noise, a high responsivity, and a fast response. Based on these perovskite PDs, a cost-efficient UWOC system was successfully demonstrated on an FPGA platform, achieving a data rate of 6.25 Mbps with a low bit error rate of 0.36%. Due to lower background noise under environment illumination, perovskite PDs exhibit better communication stability before reaching a data rate threshold; however, the BER increases rapidly due to the long fall time, resulting in difficulty in distinguishing switching signals. Reducing the fall time of perovskite PDs and using advanced coding techniques can help to further improve the performance of the UWOC system based on perovskite PDs. This work not only demonstrates the potential of perovskite PDs in the application of UWOC, but also improves the development of a cost-effective UWOC system based on FPGAs. Full article

The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones in the presence of a large amount of external environment noise, noise and drift inevitably occur. The distortion phenomenon makes further signal detection and recognition difficult. In this study, a new method for denoising MEMS vector hydrophones by combining ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) is proposed to improve the utilization of received signals. First, the main frequency of the noise signal is transformed using a Fourier transform. Then, the noise signal is decomposed by EEMD to obtain the intrinsic mode function (IMF) component. The frequency of each IMF component in the center further determines that the IMF component belongs to the noise IMF component, invalid IMF component, or pure IMF component. Then, there are pure IMF reserved components, removing noisy IMF components and invalid IMF components. Finally, the desalinated IMF reconstructs the signal through SSA to obtain the denoised signal, which realizes the denoising processing of the signal, extracting the useful signal and removing the drift. The role of SSA is to effectively separate the trend noise and the periodic vibration noise. Compared to EEMD and SSA separately, the proposed EEMD-SSA algorithm has a better denoising effect and can achieve the removal of drift. Following that, EEMD-SSA is used to process the data measured by Fenhe. The experiment is carried out by the North University of China. The simulation and lake test results show that the proposed EEMD-SSA has certain practical research value. Full article

In order to study the feasibility of forming microtexture at the surface of 7050 aluminum alloy by laser-induced cavitation bubble, and how the density of microtexture influences its tribological properties, the evolution of the cavitation bubble was captured by a high-speed camera, and the underwater acoustic signal of evolution was collected by a fiber optic hydrophone system. This combined approach was used to study the effect of the cavitation bubble on 7050 aluminum alloy. The surface morphology of the microtexture was analyzed by a confocal microscope, and the tribological properties of the microtexture were analyzed by a friction testing machine. Then the feasibility of the preparation process was verified and the optimal density was obtained. The study shows that the microtexture on the surface of a sample is formed by the combined results of the plasma shock wave and the collapse shock wave. When the density of microtexture is less than or equal to 19.63%, the diameters of the micropits range from 478 μm to 578 μm, and the depths of the micropits range from 13.56 μm to 18.25 μm. This shows that the laser-induced cavitation bubble is able to form repeatable microtexture. The friction coefficient of the sample with microtexture is lower than that of the untextured sample, with an average friction coefficient of 0.16. This indicates that the microtexture formed by laser-induced cavitation bubble has a good lubrication effect. The sample with a density of 19.63% is uniform and smooth, having the minimum friction coefficient, with an average friction coefficient of 0.14. This paper provides a new approach for microtexture processing of metal materials. Full article

Underwater wireless optical communication (UWOC) systems face challenges due to the significant temporal dispersion caused by the combined effects of scattering, absorption, refractive index variations, optical turbulence, and bio-optical properties. This collective impairment leads to signal distortion and degrades the optical receiver’s bit error rate (BER). Optimising the receiver filter and equaliser design is crucial to enhance receiver performance. However, having an optimal design may not be sufficient to ensure that the receiver decision unit can estimate BER quickly and accurately. This study introduces a novel BER estimation strategy based on a Convolutional Neural Network (CNN) to improve the accuracy and speed of BER estimation performed by the decision unit’s computational processor compared to traditional methods. Our new CNN algorithm utilises the eye diagram (ED) image processing technique. Despite the incomplete definition of the UWOC channel impulse response (CIR), the CNN model is trained to address the nonlinearity of seawater channels under varying noise conditions and increase the reliability of a given UWOC system. The results demonstrate that our CNN-based BER estimation strategy accurately predicts the corresponding signal-to-noise ratio (SNR) and enables reliable BER estimation. Full article

Underwater acoustic target recognition has always played a pivotal role in ocean remote sensing. By analyzing and processing ship-radiated signals, it is possible to determine the type and nature of a target. Historically, traditional signal processing techniques have been employed for target recognition in underwater environments, which often exhibit limitations in accuracy and efficiency. In response to these limitations, the integration of artificial intelligence (AI) methods, particularly those leveraging machine learning and deep learning, has attracted increasing attention in recent years. Compared to traditional methods, these intelligent recognition techniques can autonomously, efficiently, and accurately identify underwater targets. This paper comprehensively reviews the contributions of intelligent techniques in underwater acoustic target recognition and outlines potential future directions, offering a forward-looking perspective on how ongoing advancements in AI can further revolutionize underwater acoustic target recognition in ocean remote sensing. Full article

In this paper, we further develop a novel, efficient approach to the problem of signal detection against background noise based on a nonlinear residual functional called the neuron-like criterion function (NCF). A detailed comparison of the NCF-based technique and the conventional correlation criterion function (CCF)-based matched-signal detection is performed. For this purpose, we calculated the detection performance curves for both techniques and found the range of the problem parameters in which the NCF-based detector shows a certain advantage. The latter consists of achieving a fixed value of detection probability at a lower threshold value of the input signal-to-noise ratio (SNR) compared to the CCF-based detector. Special attention is given to the practically important scenario of receiving a weak signal against the background of non-stationary noise with a certain trend (positive or negative) of its intensity. For these two specific cases, modified NCFs are given, which are then used for computer simulation. For both broadband and narrow-band signals, the quantitative bounds of the most effective use of the derived NCFs are established and interpreted. The real sea noise data obtained from two underwater acoustic arrays, one stationary on the sea bottom and the other towed near the sea surface, are used for experimental validation. The experimental data processing results confirm the simulation results and make it possible to demonstrate the advantage of the NCF if the noise intensity shows a significant trend over the signal observation interval. The latter case obviously corresponds to the use of the towed array in the coastal area. Full article