Journal of Marine Science and Engineering

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14 pages, 5142 KiB

Open AccessArticle

Noise Reduction of Velocity Measured by Frequency-Supervised Combined Doppler Sonar Using an Adaptive Sliding Window and Kalman Filter

by Peng Liu, Bingxin Liu, Xueyuan Zhu, Peng Chen and Ying Li

J. Mar. Sci. Eng. 2024, 12(12), 2320; https://doi.org/10.3390/jmse12122320 - 18 Dec 2024

Viewed by 581

Abstract

Velocity is fundamental information for ocean engineering. It is difficult for traditional Doppler sonar to provide accurate and wide-range velocity measurement information with a short time lag. Therefore, a frequency-supervised combined Doppler sonar system using an adaptive sliding window and Kalman filter is [...] Read more.

Velocity is fundamental information for ocean engineering. It is difficult for traditional Doppler sonar to provide accurate and wide-range velocity measurement information with a short time lag. Therefore, a frequency-supervised combined Doppler sonar system using an adaptive sliding window and Kalman filter is proposed. In this method, the initial value of the integer ambiguity is calculated based on the average value of the conventional Doppler sonar. The change value of the integer ambiguity is calculated by the difference of the adjacent velocities measured by coherent Doppler sonar. The velocity of combined Doppler sonar is calculated by the cumulative result of the initial and change values of integer ambiguities. Finally, the velocity bias due to the error of the integer ambiguity calculation is corrected by the frequency supervision using the Kalman filter in a sliding time window under different signal-to-noise ratios. The experimental results show that the proposed method is more accurate than the conventional Doppler sonar, has a wider measurement range compared with coherent Doppler sonar, and suppresses the impulsive noise well. The frequency-supervised combined Doppler sonar using an adaptive sliding window and Kalman filter can provide accurate and precise velocities with a short time lag over a wide range of signal-to-noise ratios. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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15 pages, 3291 KiB

Open AccessArticle

Adaptive Tracking Method for Time-Varying Underwater Acoustic Channel Based on Dynamic Gaussian Window

by Zemin Zhou, Zhikuan Chen, Bin Wang, Yunjiang Zhao and Yi Lou

J. Mar. Sci. Eng. 2024, 12(12), 2185; https://doi.org/10.3390/jmse12122185 - 29 Nov 2024

Viewed by 708

Abstract

The traditional recursive least squares (RLS) algorithm is limited in highly dynamic and noisy underwater channels. To overcome this, we introduce the time-varying Gaussian sliding window-based RLS (VGSRLS) algorithm, designed for enhanced channel tracking. The VGSRLS algorithm adaptively adjusts window length based on [...] Read more.

The traditional recursive least squares (RLS) algorithm is limited in highly dynamic and noisy underwater channels. To overcome this, we introduce the time-varying Gaussian sliding window-based RLS (VGSRLS) algorithm, designed for enhanced channel tracking. The VGSRLS algorithm adaptively adjusts window length based on the signal’s instantaneous frequency variation. A rotation matrix reorients the Gaussian window toward the highest signal-to-noise ratio (SNR) direction, increasing channel tracking accuracy. Further, the algorithm adapts the Gaussian window shape along the highest SNR direction by combining dynamic window length and anisotropic adjustments, effectively suppressing noise from other directions and enhancing SNR. Simulation results confirm that VGSRLS achieves superior channel estimation accuracy, showing reduced mean squared deviation (MSD) under typical noise conditions in underwater environments compared to the SRLS-DCD algorithm. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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22 pages, 5932 KiB

Open AccessArticle

Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front

by Weishuai Xu, Lei Zhang, Ming Li, Xiaodong Ma and Maolin Li

J. Mar. Sci. Eng. 2024, 12(11), 2010; https://doi.org/10.3390/jmse12112010 - 7 Nov 2024

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Abstract

Ocean fronts, widespread across the global ocean, cause abrupt shifts in physical properties such as temperature, salinity, and sound speed, significantly affecting underwater acoustic communication and detection. While past research has concentrated on qualitative analysis and small-scale research on ocean front sections, a [...] Read more.

Ocean fronts, widespread across the global ocean, cause abrupt shifts in physical properties such as temperature, salinity, and sound speed, significantly affecting underwater acoustic communication and detection. While past research has concentrated on qualitative analysis and small-scale research on ocean front sections, a comprehensive analysis of ocean fronts’ characteristics and their impact on underwater acoustics is lacking. This study employs high-resolution reanalysis data and in situ observations to accurately identify ocean fronts, sound speed structures, and acoustic propagation features from over six hundred thousand Kuroshio Extension Front (KEF) sections. Utilizing marine big data statistics and machine learning evaluation metrics such as out-of-bag (OOB) error and Shapley values, this study quantitatively assesses the variations in sound speed structures across the KEF and their effects on acoustic propagation shifts. This study’s key findings reveal that differences in sound speed structure are significantly correlated with KEF strength, with the channel axis depth and conjugate depth increasing with front strength, while the thermocline intensity and depth excess decrease. Acoustic propagation features in the KEF environment exhibit notable seasonal variations. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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28 pages, 27981 KiB

Open AccessArticle

Acoustic Imaging Learning-Based Approaches for Marine Litter Detection and Classification

by Pedro Alves Guedes, Hugo Miguel Silva, Sen Wang, Alfredo Martins, José Almeida and Eduardo Silva

J. Mar. Sci. Eng. 2024, 12(11), 1984; https://doi.org/10.3390/jmse12111984 - 3 Nov 2024

Viewed by 1430

Abstract

This paper introduces an advanced acoustic imaging system leveraging multibeam water column data at various frequencies to detect and classify marine litter. This study encompasses (i) the acquisition of test tank data for diverse types of marine litter at multiple acoustic frequencies; (ii) [...] Read more.

This paper introduces an advanced acoustic imaging system leveraging multibeam water column data at various frequencies to detect and classify marine litter. This study encompasses (i) the acquisition of test tank data for diverse types of marine litter at multiple acoustic frequencies; (ii) the creation of a comprehensive acoustic image dataset with meticulous labelling and formatting; (iii) the implementation of sophisticated classification algorithms, namely support vector machine (SVM) and convolutional neural network (CNN), alongside cutting-edge detection algorithms based on transfer learning, including single-shot multibox detector (SSD) and You Only Look once (YOLO), specifically YOLOv8. The findings reveal discrimination between different classes of marine litter across the implemented algorithms for both detection and classification. Furthermore, cross-frequency studies were conducted to assess model generalisation, evaluating the performance of models trained on one acoustic frequency when tested with acoustic images based on different frequencies. This approach underscores the potential of multibeam data in the detection and classification of marine litter in the water column, paving the way for developing novel research methods in real-life environments. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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17 pages, 1379 KiB

Open AccessArticle

Range-Domain Subspace Detector in the Presence of Direct Blast for Forward Scattering Detection in Shallow-Water Environments

by Jiahui Luo, Chao Sun and Mingyang Li

J. Mar. Sci. Eng. 2024, 12(10), 1864; https://doi.org/10.3390/jmse12101864 - 17 Oct 2024

Viewed by 757

Abstract

This paper aims to detect a target that crosses the baseline connecting the source and the receiver in shallow-water environments, which is a special scenario for a bistatic sonar system. In such a detection scenario, an intense sound wave, known as the direct [...] Read more.

This paper aims to detect a target that crosses the baseline connecting the source and the receiver in shallow-water environments, which is a special scenario for a bistatic sonar system. In such a detection scenario, an intense sound wave, known as the direct blast, propagates directly from the source to the receiver without target scattering. This direct blast usually arrives at the receiver simultaneously with the forward scattering signal and exhibits a larger intensity than the signal, posing a significant challenge for target detection. In this paper, a range-domain subspace is constructed by the horizontal distance between the source/target and each element of a horizontal linear array (HLA) when the ranges of environmental parameters are known a priori. Meanwhile, a range-domain subspace detector based on direct blast suppression (RSD-DS) is proposed for forward scattering detection. The source and the target are located at different positions, so the direct blast and the scattered signal are in different range-domain subspaces. By projecting the received data onto the orthogonal complement subspace of the direct blast subspace, the direct blast can be suppressed and the signal that lies outside the direct blast subspace is used for target detection. The simulation results indicate that the proposed RSD-DS exhibits a performance close to the generalized likelihood ratio detector (GLRD) while requiring less prior knowledge of environments (only known are the ranges of the sediment sound speed and the bottom sound speed), and its robustness to environmental uncertainties is better than that of the latter. Moreover, the proposed RSD-DS exhibits better immunity against the direct blast than the GLRD, since it can still work effectively at a signal-to-direct blast ratio (SDR) of −30 dB, while the GLRD stops working in this case. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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17 pages, 10593 KiB

Open AccessArticle

A High-Precision, Ultra-Short Baseline Positioning Method for Full Sea Depth

by Yeyao Liu, Jingfeng Xue and Wei Wang

J. Mar. Sci. Eng. 2024, 12(10), 1689; https://doi.org/10.3390/jmse12101689 - 24 Sep 2024

Cited by 1 | Viewed by 1373

Abstract

To fulfill the demand for high-precision underwater acoustic positioning at full sea depth, an ultra-short baseline (USBL) positioning method with the square array based on the least squares estimating signal parameters via rotational invariance techniques (LS-ESPRIT) algorithm is presented in this paper. A [...] Read more.

To fulfill the demand for high-precision underwater acoustic positioning at full sea depth, an ultra-short baseline (USBL) positioning method with the square array based on the least squares estimating signal parameters via rotational invariance techniques (LS-ESPRIT) algorithm is presented in this paper. A combination of beam tracking and beamforming is employed to improve the accuracy of direction-of-arrival (DOA) estimation and, consequently, enhance overall positioning accuracy. In order to mitigate the issue of position jumping resulting from phase ambiguity in traditional four-element cross arrays, we have improved the stability of the positioning algorithm by utilizing a multi-element square array and employing the LS-ESPRIT algorithm for DOA estimation. Furthermore, the signal detection method integrating the correlation coefficient and ascending/descending chirp signals is employed to enhance the reliability of the location algorithm. Simulation analysis and experimental results demonstrate that the proposed algorithm effectively enhances positioning accuracy and improves the problem of jumping in positioning results. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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19 pages, 5119 KiB

Open AccessArticle

Estimation of Source Range and Location Using Ship-Radiated Noise Measured by Two Vertical Line Arrays with a Feed-Forward Neural Network

by Moon Ju Jo, Jee Woong Choi and Dong-Gyun Han

J. Mar. Sci. Eng. 2024, 12(9), 1665; https://doi.org/10.3390/jmse12091665 - 18 Sep 2024

Cited by 1 | Viewed by 1363

Abstract

Machine learning-based source range estimation is a promising method for enhancing the performance of tracking both the dynamic and static positions of targets in the underwater acoustic environment using extensive training data. This study constructed a machine learning model for source range estimation [...] Read more.

Machine learning-based source range estimation is a promising method for enhancing the performance of tracking both the dynamic and static positions of targets in the underwater acoustic environment using extensive training data. This study constructed a machine learning model for source range estimation using ship-radiated noise recorded by two vertical line arrays (VLAs) during the Shallow-water Acoustic Variability Experiment (SAVEX-15), employing the Sample Covariance Matrix (SCM) and the Generalized Cross Correlation (GCC) as input features. A feed-forward neural network (FNN) was used to train the model on the acoustic characteristics of the source at various distances, and the range estimation results indicated that the SCM outperformed the GCC with lower error rates. Additionally, array tilt correction using the array invariant-based method improved range estimation accuracy. The impact of the training data composition corresponding to the bottom depth variation between the source and receivers on range estimation performance was also discussed. Furthermore, the estimated ranges from the two VLA locations were applied to localization using trilateration. Our results confirm that the SCM is the more appropriate feature for the FNN-based source range estimation model compared with the GCC and imply that ocean environment variability should be considered in developing a general-purpose machine learning model for underwater acoustics. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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19 pages, 805 KiB

Open AccessArticle

Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems

by Lei Liu, Chao Ma, Yong Duan, Xinyu Liu and Xin Qing

J. Mar. Sci. Eng. 2024, 12(9), 1559; https://doi.org/10.3390/jmse12091559 - 5 Sep 2024

Viewed by 1292

Abstract

Orthogonal Time–Frequency Space (OTFS) is an innovative modulation method that ensures efficient and secure communication over a time-varying channel. This characteristic inspired us to integrate OTFS technology with underwater acoustic (UWA) communications to counteract the time-varying and overspread characteristics of UWA channels. However, [...] Read more.

Orthogonal Time–Frequency Space (OTFS) is an innovative modulation method that ensures efficient and secure communication over a time-varying channel. This characteristic inspired us to integrate OTFS technology with underwater acoustic (UWA) communications to counteract the time-varying and overspread characteristics of UWA channels. However, implementing OTFS in UWA communications presents challenges related to overspread channels. To handle these challenges, we introduce a specialized OTFS system and offer frame design recommendations for UWA communications in this article. We propose a Doppler compensation method and a dual-domain joint channel estimation method to address the issues caused by severe Doppler effects in UWA communication. Additionally, we propose an OTFS system detection approach. This approach incorporates an iterative detection process which facilitates soft information exchange between a message passing (MP) detector and a low-density parity check (LDPC) decoder. By conducting simulations, we demonstrate that the proposed UWA OTFS system significantly outperforms Orthogonal Frequency-Division Multiplexing (OFDM), Initial Estimate Iterative Decoding Feedback (IE-IDF-MRC), and two-dimensional Passive Time Reversal Decision Feedback Equalization (2D-PTR-DFE) in UWA channels. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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18 pages, 12105 KiB

Open AccessArticle

Underwater Mapping and Optimization Based on Multibeam Echo Sounders

by Feihu Zhang, Tingfeng Tan, Xujia Hou, Liang Zhao, Chun Cao and Zewen Wang

J. Mar. Sci. Eng. 2024, 12(7), 1222; https://doi.org/10.3390/jmse12071222 - 20 Jul 2024

Cited by 2 | Viewed by 2282

Abstract

Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study [...] Read more.

Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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18 pages, 5117 KiB

Open AccessArticle

Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging

by Jing Li, Jin Fu and Nan Zou

J. Mar. Sci. Eng. 2024, 12(3), 454; https://doi.org/10.3390/jmse12030454 - 5 Mar 2024

Cited by 1 | Viewed by 1442

Abstract

The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic [...] Read more.

The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic positioning. Machine learning (ML) combined with underwater acoustics has emerged as a prominent area of research in recent years. From a statistical perspective, ML can be viewed as an optimization strategy. Nevertheless, the existing ML-based direct-signal discrimination approaches rely on independent assessment, utilizing a single sensor (beacon or buoy), which is still insufficient for adapting to the complex underwater environment. Thus, discrimination accuracy decreases. To address the above issues, an accurate CW direct signal detection approach is performed using the decision tree algorithm, which belongs to ML. Initially, the pulse parameter characteristics in the underwater multipath channel are investigated and the parameter models are built. Then, based on multi-sensor localization performance feedback, fusion characteristics for diverse pulse are created. Next, the pulse parameter characteristics are preprocessed to mitigate the impact of varying magnitudes and units of magnitude on data processing. Then, the decision tree is built to obtain the desired output results and realize accurate recognition of the ranging direct signals. Finally, the feasibility and reliability of this paper’s method are verified by computer simulation and field testing. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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Review

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24 pages, 1450 KiB

Open AccessReview

Roadmap for Recommended Guidelines of Leak Detection of Subsea Pipelines

by Ahmed Reda, Ramy Magdy A. Mahmoud, Mohamed A. Shahin, Chiemela Victor Amaechi and Ibrahim A. Sultan

J. Mar. Sci. Eng. 2024, 12(4), 675; https://doi.org/10.3390/jmse12040675 - 18 Apr 2024

Cited by 6 | Viewed by 3336

Abstract

The leak of hydrocarbon-carrying pipelines represents a serious incident, and if it is in a gas line, the economic exposure would be significant due to the high cost of lost or deferred hydrocarbon production. In addition, the leakage of hydrocarbon could pose risks [...] Read more.

The leak of hydrocarbon-carrying pipelines represents a serious incident, and if it is in a gas line, the economic exposure would be significant due to the high cost of lost or deferred hydrocarbon production. In addition, the leakage of hydrocarbon could pose risks to human life, have an impact on the environment, and could cause an image loss for the operating company. Pipelines are designed to operate at full capacity under steady-state flow conditions. Normal operations may involve day-to-day transients such as the operations of pumps, valves, and changes in production/delivery rates. The basic leak detection problem is to distinguish between the normal operational transients and the occurrence of non-typical process conditions that would indicate a leak. To date, the industry has concentrated on a single-phase flow, primarily of oil, gas, and ethylene. The application of a leak-monitoring system to a particular pipeline system depends on environmental issues, regulatory imperatives, loss prevention of the operating company, and safety policy rather than pipe size and configuration. This paper provides a review of the recommended guidance for leak detection of subsea pipelines in the context of pipeline integrity management. The paper also presents a review of the capability and application of various leak detection techniques that can be used to offer a roadmap to potential users of the leak detection systems. Full article

(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)

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