Underwater Source Research Articles

Underwater source ranging by Siamese network aided semi-supervised learning.

Underwater source ranging based on Deep Learning methods demands a considerable amount of labeled data, which is costly to collect. To alleviate this challenge, semi-supervised learning of the wrapper paradigm is introduced into this task. First, the Siamese network is used to generate pseudo labels for unlabeled data to expand the labeled dataset. A new effective confidence criterion based on similarity score and similar sample distribution is proposed to evaluate the reliability of pseudo labels. Then the model can be trained more fully with an expanded dataset. Experiments on the SwellEx-96 dataset validate that this method can effectively improve prediction accuracy.

The Influence of the A Priori Uncertainty of the Shallow Sea Sound Channel Model on the Vertical Array Gain

The purpose of this paper is to numerically demonstrate and comparatively analyze the critically strong and ambiguous impact of the a priori uncertainty of a shallow sea waveguide model in its main physical parameters on the output performance of model-based methods for spatial processing of multimode signals received by a vertical antenna array. The scenario is specified when a relatively weak signal of a remote underwater source is received against the background of intensive interference excited by a subsurface source (like a ship, for example) and ambient sea noise excited by wind waves. The considered array processing methods include matched-signal processing, optimal processing of the signal on the background of interference and noise, and suboptimal processing based on matched-mode array filtering of one of the signal-carrying modes with adaptive selection of its number. Quantitative estimates are obtained from above for the environment uncertainties, or model errors, with respect to the sound velocity in the water column and geoacoustic parameters of the underlying bottom, at which the array gain loss does not exceed a given level. It is shown that such estimates are very different both for different environmental parameters and for processing methods, with the determining role played by the conditions of useful signal reception, namely, the modal composition and intensity levels of the interference and sea noise at the array input. The problem statement and results are considered to be useful to detail the requirements for operational oceanography tools designed to support the effective operation of sonar antenna systems in real sea environments.

On the possibility of detection and classification of noise sources based on analysis of their trajectories at the output of adaptive spatial processing

To detect and classify objects, the source trajectories detected in the process of hydroacoustic observation are used, which contain information about the measured parameters of objects, which are their classification attributes. The analysis of these attributes allows to make a decision about the class of the observed object, for example, a surface or underwater source. As the measured object parameters are used their energy characteristics, parameters of the observed trajectory (bearing, speed of change in bearing and other possible trajectory parameters). In this case, the correctness and speed of the decision on classification depends on the quantity and quality of used classification signs, which are determined by both parameters of the observed object, and the features of sound propagation from the source to the observation device.For signal detection and resolution, we further consider fast projective adaptive algorithms, the use of which, as applied to the problems of in-situ experimental signal detection was considered in [1], [4]. The goal of this class of algorithms is to provide a high probability of detection and accurate measurement of the parameters of the source trajectories under the conditions of a model of multi-beam propagation and scattering in the real ocean environment [5]-[8]. The proposed work is a continuation of the work [1], [4], and aims to ensure the use of experimental field data not only for detection, but also for the classification of the observed sources.The subject of the study are the results of the full-scale experiment of hydroacoustic noise direction finding, given earlier and described in detail in [1], [4]. For the experiment was used antenna of L = 56 vertical daisy chains (of 10 elements each), equidistantly spaced horizontally. The antenna was installed at a depth of 200 meters in the coastal marine zone of the coastal wedge near the shipping lanes. The elements of the flat antenna were affected by signals from surface ships moving ncontrollably in the observation area and one underwater source.A singular decomposition of sampled antenna element data was used to construct adaptive algorithms. Modification of the initial results of singular decomposition allows to create algorithms that provide priority conditions for extraction of separate components of the observed (e. g., weakest) signals when constructing direction finding terrain.In this regard, in addition to the non-adaptive direction finding relief, it is proposed to form three variants of direction finding relief, each of which solves part of the general problem of selection and classification of individual varieties of observed signals:– Initial, corresponding to the energy of the signals of the input sample with amplified components of the weakest signals (overview algorithm);– direction finding relief, which uses an algorithm to detect weak and scattered signals;– direction finding terrain, which highlights the coherent components of signals.Analysis of the trajectories of more than 30 sources in the episode of two hours and forty minutes has been carried out, which increased the reliability of the detection and measurement accuracy of the parameters of the observed objects. Joint analysis of the trajectories of sources based on different variants of the bearing relief allowed to improve the conditions for the detection of weak signals and to make classification decisions using the classification attribute of the width of the area of fluctuations of the surface target trajectory for signals with a strong scattered component.

A feature-compressed multi-task learning U-Net for shallow-water source localization in the presence of internal waves

The spatiotemporal variation of sound speed profile caused by internal waves usually causes the mismatch in the Matched-Field Processing (MFP) source localization. A feature-compressed multi-task learning U-Net with Convolutional Block Attention Module (MTL-UNET-CBAM) is proposed to estimate the range and depth of underwater sources in the South China Sea environment with the presence of internal waves. To handle the mismatch caused by internal waves, the temperature sensor chain data are used to reconstruct the two-dimensional sound speed profiles (2D-SSPs) based on the 2D advection model. Then, 2D-SSPs are used to generate the training set with the parabolic equation method. Sensitivity analysis is investigated to examine the effects of sound speed profile mismatch on the source localization performance. The simulation result shows the higher robustness of MTL-UNET-CBAM to the sound speed profile mismatch compared with the conventional Matched-Field Processing (CMFP) method. Experiment data in the South China Sea also used to validate the source localization performance of MTL-UNET-CBAM.

Smart Underwater Pollution Detection Based on Graph-Based Multi-Agent Reinforcement Learning Towards AUV-Based Network ITS

The exploitation/utilization of marine resources and the rapid development of urbanization along coastal cities result in serious marine pollution, especially underwater diffusion pollution. It is a non-trivial task to detect the source of diffusion pollution, such that the disadvantageous effect of the pollution can be reduced. With the vision of 6G framework, we employ Autonomous Underwater Vehicle (AUV) flock and introduce the concept of AUV-based network. In particular, we utilize the Software-Defined Networking (SDN) technique to update the controllability of the AUV-based network, leading to the paradigm of SDN-enabled multi-AUVs network Intelligent Transportation Systems (SDNA-ITS). For SDNA-ITS, we utilize artificial potential field theories to model the control model. To optimize the system output, we introduce the graph-based Soft Actor-Critic (SAC) algorithm, i.e., a category of Multi-Agent Reinforcement Learning (MARL) mechanism where each AUV can be regarded as a node in a graph. In particular, we improve the optimization model based on Centralized Training Decentralized Execution (CTDE) architecture with the assistance of the SDN controller, by which each AUV can efficiently adjust its speed towards the diffusion source. Further, to achieve exact path planning for detecting the diffusion source, a dynamic detection scheme is proposed to output the united control policy to schedule the SDNA-ITS dynamically. Simulation results demonstrate that our approaches are available to detect the underwater diffusion source when the actual scenario is taken into account and perform better than some recent research products.

Обеспечение экологической безопасности акваторий Российского арктического шельфа и повышение безопасности морских экосистем за счет снижения шумового загрязнения

Object and purpose of research. Ensuring the environmental safety of the Russian shelf waters requires constant development of methods and technologies related to monitoring, quantitative assessment and reduction of the harmful effects of pollution sources. During the industrial and transport development of the water area of the Northern Sea Route (NSR), an intensive increase of underwater noise is observed. Obtaining an adequate picture of the impact of underwater noise sources on representatives of the marine ecosystem creates a need to involve more and more modern and accurate measurement techniques. The article describes the draft methods for measuring the self-noise of carrier vessel and underwater noise of a selected marine equipment object, which were developed in order to identify the degree of impact of noise pollution over to the ecological situation in the considered water area. Materials and methods. An hydro acoustic signals contains the self-noise of measuring vessel, the noise of an object of marine industrial equipment (OMIE) and the noise of the water area where acoustic tests are going on. This acoustic signals are perceived by combined sound pressure and sound pressure gradient transducers, as well as by an omnidirectional hydrophone, located in the receiving system , and are converted into electrical signals transmitted via the main cable to the onboard post of the measuring vessel. Signals are sent to the hydro acoustic guidance beacon via the same cable to control the operation of this beacon. The information processing procedure is set out in the SIGAK VP Operation Manual (MGFK.411711.327 RE). The main purpose of the primary processing is to obtain 1/3-octave spectra and levels of underwater noise generated by the tested carrier vessel or OMIE, as well as the noise of the water area where these tests are carried out. In accordance with the accepted classification, the method of measuring sound pressure levels using a hydro acoustic complex according to this technique refers to indirect methods with single observations. Main results. In the course of the work, preliminary methods for measuring the self-noise of measuring vessel and underwater noise of a selected marine industrial object were developed. To measure the levels of underwater noise in 1/3-octave frequency bands in the range from 5 Hz to 10,000 Hz, within the framework of the draft methods, it is planned to use a stationary measuring hydro acoustic complex with a vector receiver from the GIK-VP. The signal processing algorithms are based on the spatial-frequency filtering of the acoustic power flux density components, which makes it possible to protect the measurement information from interference signals whose propagation direction does not coincide with the direction to the measured object. Conclusion. The results obtained in the form of implemented preliminary methods are important for the creation of regulatory documentation for the regulation of technogenic underwater noise in the waters of the Russian jurisdiction, reducing the intensity of noise pollution and the detrimental impact over to marine ecosystems. The article targeting the support at the stages of acoustical marine technical design, construction and operation of offshore industrial facilities and ships of various types, acoustic monitoring of the compliance of marine equipment with international standards for underwater noise.

Underwater Small Target Detection Based on YOLOX Combined with MobileViT and Double Coordinate Attention

The underwater imaging environment is complex, and the application of conventional target detection algorithms to the underwater environment has yet to provide satisfactory results. Therefore, underwater optical image target detection remains one of the most challenging tasks involved with neighborhood-based techniques in the field of computer vision. Small underwater targets, dispersion, and sources of distortion (such as sediment and particles) often render neighborhood-based techniques insufficient, as existing target detection algorithms primarily focus on improving detection accuracy and enhancing algorithm complexity and computing power. However, excessive extraction of deep-level features leads to the loss of small targets and decrease in detection accuracy. Moreover, most underwater optical image target detection is performed by underwater unmanned platforms, which have a high demand of algorithm lightweight requirements due to the limited computing power of the underwater unmanned platform with the mobile vision processing platform. In order to meet the lightweight requirements of the underwater unmanned platform without affecting the detection accuracy of the target, we propose an underwater target detection model based on mobile vision transformer (MobileViT) and YOLOX, and we design a new coordinate attention (CA) mechanism named a double CA (DCA) mechanism. This model utilizes MobileViT as the algorithm backbone network, improving the global feature extraction ability of the algorithm and reducing the amount of algorithm parameters. The double CA (DCA) mechanism can improve the extraction of shallow features as well as the detection accuracy, even for difficult targets, using a minimum of parameters. Research validated in the Underwater Robot Professional Contest 2020 (URPC2020) dataset revealed that this method has an average accuracy rate of 72.00%. In addition, YOLOX’s ability to compress the model parameters by 49.6% efficiently achieves a balance between underwater optical image detection accuracy and parameter quantity. Compared with the existing algorithm, the proposed algorithm can carry on the underwater unmanned platform better.

Remote Water-to-Air Eavesdropping with a Phase-Engineered Impedance Matching Metasurface.

Efficiently receiving underwater sound remotely from air is a long-standing challenge in acoustics hindered by the large impedance mismatch at the water-air interface. Here, a phase-engineered water-air impedance matching metasurface is proposed and experimentally demonstrated for remote and efficient water-to-air eavesdropping. The judiciously designed metasurface with near-unity transmission efficiency, long monitoring distance, and high mechanical stiffness is capable of making the water-air interface acoustically transparent and, at the same time, freewheelingly patterning the transmitted wavefront. This enables efficient control over the effective spatial location of a distant airborne sensor such that it can measure underwater signals with large signal-to-noise ratios as if placed close to the physical underwater source. Such airborne eavesdropping of underwater sound is experimentally demonstrated with a measured sensitivity enhancement of nearly 104 at 8kHz, far from achievable with the current state-of-the-art methods. Moreover, the opportunities of using the proposed metasurface for cross-media orbital-angular-momentum-multiplexed communication and underwater acoustic window are also demonstrated. This metasurface opens new avenues for communication and sensing in inhomogeneities with totally reflective interfaces, which may be translated to nano-optics and radio frequencies.

Integrating modeled environmental variability into neural network training for underwater source localization.

Supervised machine learning (ML) is a powerful tool that has been applied to many fields of underwater acoustics, including acoustic inversion. ML algorithms depend on the existence of extensive labeled datasets, which are difficult to obtain for the task of underwater source localization. A feed-forward neural network (FNN) trained on imbalanced or biased data may end up suffering from a problem analogous to model mismatch in matched field processing (MFP), that is, producing incorrect results due to a difference between the environment sampled by the training data and the actual environment. To overcome this issue, physical and numerical propagation models can act as data augmentation tools to compensate for the lack of comprehensive acoustic data. This paper examines how modeled data can be effectively used for training FNNs. Mismatch tests compare the output from a FNN and MFP and show that the network becomes more robust to various kinds of mismatches when trained on diverse environments. A systematic analysis of how the training dataset's variability impacts a FNN's localization performance on experimental data is carried out. Results show that networks trained with synthetic data achieve better and more robust performance than regular MFP when environment variability is taken into account.

A Multi-Round Game-Based Source Location Privacy Protection Scheme With AUV Enabled in Underwater Acoustic Sensor Networks

With a growing demand for the development of smart ocean projects, the Internet of Underwater Things (IoUT) integrating multiple networks will gradually become the mainstream, particularly for civil, commercial, and military scenarios. During development, however, the security aspect cannot be ignored. Faced with possible future battles on the ocean, this study focuses on underwater source location privacy protection for both static and mobile sources. By combining the Ekman drift model, location privacy protection is converted into multi-round gaming between the source and adversary, in which two ways of gaming on nodes, autonomous underwater vehicle (AUV), and adversary are adopted to protect the static source location. Because the location of the mobile source, such as underwater intelligent agent, changes regularly, the protection for this type of source is focused on the trajectory, preventing the mobile source from being tracked. This paper aims to protect the location privacy of the static source first and then initial attempts to protect the dynamic source location privacy. By fuzzing the remaining semantic information after the mobile source passes by, the trajectory of the mobile source is protected, thereby protecting its location privacy. Compared with existing algorithms, the simulation results of the proposed scheme indicate the probability that the source has been captured. Furthermore, the results show that the period of safety of the proposed algorithm is longer than that of a multi-hop-based location privacy protection scheme but shorter than that of an autonomous underwater vehicle based scheme. Although the difference in the energy consumption of the nodes among the compared schemes is little increased, the delay varies significantly.

On the Reproducibility and Directionality of Underwater Laser-induced Acoustic Signal

An intense laser pulse focused in water could induce plasma expansion, which can be used as an impulsive sound source. The optoacoustic underwater source is very useful in marine applications; however, the poor reproductivity and the sharp directivity of the emitted signals is a big problem. The reproductivity and directivity of the acoustic emission by laser breakdown are measured in the laboratory experiment in this paper, and the results show that the reproductivity and directivity are both direction-dependent. The measurement also shows the feasibility of improving the reproductivity and directivity by adjusting the focusing system of the laser beam.

Performance Assessment of the Innovative Autonomous Tool CETOSCOPE© Used in the Detection and Localization of Moving Underwater Sound Sources

The detection and localization of acoustic sources remain technological challenges in bioacoustics, in particular, the tracking of moving underwater sound sources with a portable waterproof tool. For instance, this type of tool is important to describe the behavior of cetaceans within social groups. To contribute to this issue, an original innovative autonomous device, called a CETOSCOPE©, was designed by ABYSS NGO, including a 360° video camera and a passive acoustic array with 4 synchronized hydrophones. Firstly, different 3D structures were built and tested to select the best architecture to minimize the errors of the localizations. Secondly, a specific software was developed to analyze the recorded data and to link them to the acoustic underwater sources. The 3D localization of the sound sources is based on time difference of arrival processing. Following successful simulations on a computer, this device was tested in a pool to assess its efficiency. The final objective is to use this device routinely in underwater visual and acoustic observations of cetaceans.

High-resolution passive depth estimation for broadband sources in deep water based on orthogonal matching pursuit algorithm

The propagation mode of reliable acoustic path (RAP) in deep water is widely used in the field of target detection. A receiver set at the bottom of the seabed can receive acoustic signals from RAP. The direct (D) wave and the surface-reflected (SR) wave from a broadband source form an undulating interference structure in the frequency domain. This feature is sensitive to target depth and is thus used for depth estimation. Traditionally, depth estimation using the interference pattern, the scalar intensity in the frequency domain is processed by the optimal Fourier transform to extract the line spectrum in the depth domain. Based on this method, we proposed a passive depth estimation method for underwater broadband sources in deep water based on the orthogonal matching pursuit (OMP) algorithm. The OMP-based method is applied to scalar vertical line array (VLA) and single vector sensor (SVS) data. The OMP-based method largely eliminates side lobe interference, reduces the width of the main lobe, and improves depth estimation resolution while ensuring the stability and reliability of the results. Finally, the experimental data of VLA and SVS in the South China Sea are processed to validate the proposed method.

Research on Information Transmission Technology Through the Water–Air Interface Combining a Sonar and a Radar

In order to realize the information transmission across the water-air interface, the radar has been used to detect the tiny surface perturbations caused by the underwater low-frequency sound source. Due to the differences of medium properties at the water-air interface, it is difficult for underwater targets to communicate with airborne platforms directly. In order to solve this problem, we propose the finite element method to establish the minute vibration model on the water surface caused by the underwater low-frequency sound source, and analyze the influence of sound source parameters on the tiny surface perturbations at the surface of water. Then we design the frame structure and synchronization signal of the single-carrier communication system, and propose a robust information transmission method across the water-air interface. Finally, we experimentally verify the information transmission method combining a low-frequency sonar and a millimeter-wave radar in an anechoic water tank. The experimental results show that the method we proposed can realize the information transmission across the water-air interface up to 500 bps.

Study on Directivity of Underwater Circular Transducer Array

At present, in the detection and identification of underwater noise sources, one of the most commonly used transducer arrays is the circumferential transducer array. The circumferential transducer array is symmetrical in space and has high withstand voltage capability and small sound pressure fluctuation in the horizontal beam. Its directivity is an important index that affects the performance of the array. This paper focuses on the directivity maps of circular arrays with different sizes and numbers of elements. From the graph, it is concluded that the undulation of the directivity graph of the circular array is directly related to the array element spacing. The smaller the array element spacing is, the smaller the undulation is, and vice versa. The directivity formula and law can provide technical guidance for the design of a uniform circular transducer array.

Geoacoustic Inversion Using Simple Hand-Deployable Acoustic Systems

This article proposes the use of a simple, low-cost, hand-deployable pair of experimental assets to conduct geoacoustic inversion at sea. The system consists of an expendable, fully mechanical acoustic source called a rupture induced underwater sound source (RIUSS) and a new ropeless passive acoustic mooring called a TOSSIT (not an acronym). Used together, RIUSS and TOSSIT enable the collection of acoustic data suitable to perform single-hydrophone geoacoustic inversion. The method is illustrated using data collected on the New England Mud Patch in May 2021 from a relatively small (22 m) and inexpensive chartered fishing vessel. Modal time-frequency dispersion from 15 to 385 Hz is extracted from the TOSSIT/RIUSS data using warping, and used as input for Bayesian transdimensional geoacoustic inversion. The inversion results compare favorably to results obtained with data collected on the same track with traditional assets (e.g., a vertical line array) during the 2017 Seabed Characterization Experiment, even when jointly inverting for the water-column sound speed profile and seabed geoacoustic parameters. This further demonstrates inversion repeatability in a given location using data sets collected years apart, and under different (and potentially unknown) oceanographic conditions.

Modified you-only-look-once model for joint source detection and azimuth estimation in a multi-interfering underwater acoustic environment

The you-only-look-once (YOLO) model identifies objects in complex images by framing detection as a regression problem with spatially separated boundaries and class probabilities. Object detection from complex images is somewhat similar to underwater source detection from acoustic data, e.g., time-frequency distributions. Herein, YOLO is modified for joint source detection and azimuth estimation in a multi-interfering underwater acoustic environment. The modified you-only-look-once (M-YOLO) input is a frequency-beam domain (FBD) sample containing the target and multi-interfering spectra at different azimuths, generated from the received data of a towed horizontal line array. M-YOLO processes the whole FBD sample using a single-regression neural network and directly outputs the target-existence probability and spectrum azimuth. Model performance is assessed on both simulated and at-sea data. Simulation results reveal the strong robustness of M-YOLO toward different signal-to-noise ratios and mismatched ocean environments. As tested on the data collected in an actual multi-interfering environment, M-YOLO achieved near-100% target detection and a root mean square error of 0.54° in azimuth estimation.

A method of underwater sound source range estimation without prior knowledge based on single sensor in shallow water

Introduction: The lack of prior knowledge of the marine environment increases the difficulty of passive ranging of underwater sound sources by using a single hydrophone. The dispersion curve of the normal mode contains extensive marine environmental information, which can be extracted without prior knowledge, but the characteristics of dispersion curves of different modes vary, and the mode order cannot be determined from the received data.Methods: Herein, a method based on a single hydrophone that can jointly identify the mode order and estimate the propagation range in unknown marine environment is proposed. The method uses Bayesian theory as the main methodology and is applicable to broadband pulse sound sources in shallow seas with long-range propagation. The dispersion curves extracted from the data and those calculated by the dispersion formula are the input signal and the replica of the methods, respectively. Accurate identification of the normal mode order and estimation of the propagation range can be achieved by establishing the joint cost function.Results: In the case of unknown a priori knowledge of the marine environment, the method enables rapid inversion, is tolerant to environmental parameter mismatch, and is low cost and practical.Discussion: The simulation and measured data analysis results demonstrate the accuracy and validity of the method. The measured data contains linear frequency modulation impulse source signal and explosion sound source signals, and the mean relative error of range estimation is less than 5%.

Semi-supervised Learning Method for Source Range Estimation in Shallow Water

Data-driven machine learning methods in recent years have shown great vitality in underwater acoustic, especially in the localization of underwater sound sources. Meanwhile, there exists a great difficulty in the acquisition and processing of underwater acoustic data, which caused the lack of label data. This paper proposes a two-step semi-supervised learning-based range estimation method for shallow ocean sound sources, which utilizes semi-supervised learning in deep learning to alleviate the problem of limited access to underwater acoustic label data and relatively abundant unlabelled data in practical scenarios. The performance of the method has been validated on SWellEx-96 experiment data.

ALMA 2022 experiment : Source paramater estimation comparison betweenLFM and MLS waveforms

For underwater source localization, accurate estimation of the time of flight (or travel time) is required and the observed waveform may include Doppler (waveform dilation). The source waveform, thus, needs to be sensitive to estimate both time of flight and Doppler. In this study, we compare two waveforms : Linear Frequency Modulated sweeps (LFMs) and Maximum Length Sequences (MLSs) with identical center frequency (6 kHz) and bandwidth (2 kHz). During an at-sea experiment conducted in March 2022 near in the Mediterranean Sea, near the coast of Toulon, a mobile source transmitted the two waveforms, recorded by a 192-hydrophone moored bilboard array. The source-array distance ranged from 2.5 to 8 km, and the source was towed at a constant speed (∼1.5 m/s) and depth (∼50m). The analysis of the gathered data shows that the MLS waveform outperforms the LFM waveform for estimating time of flight (uncertainty reduced by a factor 4) and radial velocity (uncertainty reduced by a factor 20). Preliminary results focus on single hydrophone processing, but possible applications to 2D array processing are discussed for localizing the source in range-dependent environments.