Sonar Operations Research Articles

Wideband beam domain sparse Bayesian learning passive focusing localisation algorithm

AbstractTo address the challenges of large‐aperture sonar systems passive localisation, this paper proposes the application of sparse Bayesian learning (SBL) for passive target localisation in the wideband beam domain. The proposed algorithm aims to overcome the issues of massive computational requirements for two‐dimensional SBL scanning and increased localisation errors due to interference energy leakage. The wideband beam domain SBL focusing localisation algorithm is developed by constructing an azimuth‐range two‐dimensional transformation matrix to preprocess array data, which effectively reduces the computational load of SBL processing while suppressing strong interference energy leakage in passive sonar operating environments, thus improving the range resolution and parameter estimation accuracy of focusing localisation. Simulation and sea trial data analyses demonstrate the feasibility of the proposed algorithm, with results indicating its superior performance compared to existing algorithms.

Isogeometric modeling and vibro-acoustic analysis of flow-excited irregular cavity-plate-exterior space coupled system

The flow-induced noise has become an important noise source in marine sonar self-noise, which can adversely affect the normal operation of sonar. The marine sonar cabin is simplified as a cavity-plate-exterior space coupled system whose flow-induced vibro-acoustic characteristics are investigated in this paper. An isogeometric vibro-acoustic formulation is proposed in which the cavity with an irregular geometry is precisely described by adjusting the control points and corresponding weights. The flow-induced vibro-acoustic response is obtained by transferring the turbulent pressure data from computational fluid dynamics into the isogeometric vibro-acoustic model. Imposing turbulent pressure into the isogeometric control points is proposed to achieve this objective using a node-based interpolation method. The vibro-acoustic modeling is validated and compared with previous experimental results. These comparisons demonstrate that the developed formulation accurately predicts the vibro-acoustic characteristics of the fluid-excited coupled system. The influences of flow speed, acoustic medium, and cavity shape on flow-excited vibration and sound radiation are discussed. Results show a decrease in radiated acoustic power and radiation efficiency in the exterior space, and a shift in the plate-exterior space coupling modal frequency to lower frequencies when the cavity changes from convex to concave.

Performance During a Task That Simulates Passive Sonar Operator Duties Under Conditions of Varying Workloads.

It is critical to develop and implement lab-based computer experiments that simulate real-world tasks in order to characterize operational requirements and challenges or identify potential solutions. Achieving a high degree of laboratory control, operational generalizability, and ease-of-use for a task is challenging, often leading to the development of tasks that can satisfy some facets but not all. This can result in insufficient solutions that leave real-world stakeholders with unsolved problems. This issue is addressed using a customized passive sonar simulator application that provides extensive researcher control over the design and manipulation of a sonar task; a visual appearance and cognitive demand similar to a true submarine-based sonar task; and a convenient and short training routine for sonar novices. The task requires participants to watch for multiple signal sources of varying appearance and salience and subsequently classify these signals into their respective categories. The current study investigated the effects of stimulus signal strength and signal density on sonar task performance-including metrics of classification accuracy, classification confidence, and response times-finding an interaction between signal density and signal strength that resulted in greater performance errors with high signal density at the weakest signal strength. The lab-based sonar application provides new possibilities for research, not limited to signal intensity and signal density but also through the manipulation of parameters such as the number of unique targets, target appearance, and task duration. This application may illuminate the operational demands that each of these factors may have on operator behavior within the dynamic tasks.

Underwater Acoustic Target Classification Using Convolutional Neural Network Combined with Continuous Wavelet Transform

Underwater acoustic target (UAT) classification is a critical task in submarine warfare because acoustic waves are the reliable information source that allows sonar operators and commanders to understand the surrounding situation in the operational area. To solve the problem of UAT classification, this paper proposes a convolutional neural network combined with continuous wavelet transform for improving the accuracy of UAT classification. The classification focuses on types of noise emitted from the propellers of different ships. Signal processing methods such as Short Time Fourier Transform, Continuous Wavelets Transform, and CNN are executed to extract signal features that provide information for classifier. Simulation results are achieved with an accuracy of 99.64\% when using the Continuous Wavelets Transform method combined with the convolutional neural network, which is higher than other traditional methods.

High-fidelity simulations of boundary layer transition on an underwater axisymmetric body with stability theory

During the cruise status of an underwater high-speed vehicle, the flow around the vehicle's head typically transitions from a laminar to a turbulent state, triggering flow noise that can interfere with the normal operation of sonar. In order to accurately investigate this flow noise through numerical simulation, a high-fidelity turbulent flow field solution is essential. Common traditional turbulence numerical simulation methods, such as unsteady Reynolds-averaged simulation and large eddy simulation (LES), struggle to capture high-frequency turbulent fluctuations accurately due to their inability to directly resolve small-scale eddy structures, which results in compromising the simulation accuracy of high-frequency flow noise. To address this issue, this paper employs direct numerical simulation (DNS) to achieve high-fidelity resolution of the turbulent flow field, thereby enabling a more accurate assessment of flow noise distribution on the vehicle's surface. Meanwhile, considering significant computational resources required to solve the entire flow field in an underwater high Reynolds number environment, this study also incorporates the fixed transition modeling method and stability theory to confine the DNS computational domain to the vicinity of the transitional zone to improve simulation efficiency. Comparative analysis of flow noise monitoring results in the laminar, transitional, and turbulent zones revealed that the flow noise source in the laminar zone exhibits the lowest amplitude across all frequencies, while the flow noise source in the transitional zone features the highest amplitude, approximately 10 dB higher than that in the turbulent zone. Moreover, significant amplitudes in high-frequency components (above 30 kHz) are detected in both the transition and turbulent zones. Additionally, this study employs LES with the Smagorinsky model to simulate the flow field within the same computational domain as DNS, demonstrating the limitations of the Smagorinsky model-based LES in capturing high-frequency flow noise.

Deep Learning Based Classification of Underwater Acoustic Signals

The classification of underwater acoustic targets is a challenging task due to the intricacies involved in soundscape, huge amounts of background interference, and the varied features of underwater targets. Various approaches have been explored for classifying underwater targets based on acoustic signatures. The problem with these conventional methods lies in the extensive domain-specific knowledge they demand for effective feature engineering. Alternatively, the deep learning approach is of great appeal for assistance to manual sonar operators who mainly rely on their expertise for the classification of targets. This work leverages the approach of deep neural networks for underwater acoustic target categorization, aiming to analyze whether we can reduce the need for extensive domain knowledge required, by letting deep learning algorithms itself find the deep audio embeddings. This approach implements an audio classifier designed to classify acoustic targets with input as log Mel-spectrograms. To compare the performance of this classifier, we also implement a regular neural network for audio classification with MFCCs as input features. The overall results of the model are promising.

Technical Design and Data Analysis of Autonomous Underwater Vehicle-Based Side-Scan Sonar Operations

In response to the absence of standardized work practices, work safety measures, efficient work procedures, and suitable line planning methods for exploring seabed topography using autonomous underwater vehicles (AUVs) equipped with side-scan sonar systems, this paper proposes a design framework for side-scan sonar measurement technology based on AUVs. Detailed research has been conducted on line planning methods that differ significantly from those used in shipborne operations. The new line planning method was developed based on the performance characteristics of AUV equipment, the principles of motion modes, and shipborne side-scan sonar operations. The validity of the line planning method was verified through actual sea experiments using different types of AUVs in various terrains. The experimental results demonstrate that the new survey line planning method is distinct from existing AUV path planning methods and is highly applicable for conducting seabed topography exploration operations. Furthermore, the technical design scheme studied in this article is applicable in different sea areas and ensures the safety, operational efficiency, and data quality of AUVs. It holds significant importance for marine science engineering and practical applications. The article also features an analysis of successful data collected from AUVs equipped with side-scan sonar for conducting seabed topography exploration operations.

The extreme bulk-modulus contrast of bubbly soft matter for underwater acoustic modulation

Bubbles in liquid and soft matter are excellent acoustic resonators with extremely low resonant frequencies. We find that bubbly soft matter (BSM) can enable full-phase (2π) modulation of incident acoustic waves just through changing the bubble size. Compared with the conventional solid–solid binary locally resonant metamaterial (SSM), due to the extreme bulk-modulus ratio of the bubbles and the soft matrix, the BSM has two distinguished merits. First, the size of the bubbly inclusions for the full-phase modulation is only about a third of the solid inclusions in the SSM; second, the range of variation of the bubbly inclusions to cover the full-phase modulation is two orders of magnitude wider than that of the solid inclusions. These features, combined with the salient advantage of wave-impedance match between soft matter and water, can realize compact and light-weight reflective and transmissive underwater bubble metasurfaces. At the lower limit 3 kHz of the active sonar operating frequency, an ultrathin reflective metasurface with a thickness of 1/50 the wavelength can redirect the incident waves at a given angle. Bubble resonance in soft matter opens a new avenue for underwater acoustic modulation at the deepest subwavelength.

Novel DEMON Spectra Analysis Techniques and Empirical Knowledge Based Reference Criterion for Acoustic Signal Classification

This paper presents some novel methods to estimate a vessel’s number of shafts, course, speed and classify it using the underwater acoustic noise it generates. A classification framework as well as a set of reference parameters for comparison are put forth. Identifying marine traffic in surroundings is an important task for vessels in an open sea. Vessels in vicinity can be identified using their signatures. One of the typical signatures emitted by a vessel is its acoustic measurements. The raw sonar data consisting of the acoustic signatures is generally observed manually by sonar operators for suggesting class of query vessel. The valuable information that can be extracted from the recorded acoustic signature includes shaft revolutions per minute (SRPM), number of blades (NOB), number of shafts, course and speed etc. Expert sonar operators use their empirical knowledge to estimate a vessel’s SRPM and NOB. Based on this information vessel classification is performed. Empirical knowledge comes with experience, and the manual process is prone to human error. To make the process systematic, calculation of the parameters of the received acoustic samples can be visually analyzed using Detection of Envelope Modulation on Noise (DEMON) spectra. Reported research mostly focuses on SRPM and NOB. Parameters such as number of shafts and vessel course and speed can effectively aid the vessel classification process. This paper makes three novel contributions in this area. Firstly, some novel DEMON spectra analysis techniques are proposed to estimate a water vessel’s number of shafts, speed, and relative course. Secondly, this paper presents a classification framework that uses the features extracted from DEMON spectra and compares them with a reference set. Thirdly, a novel set of reference parameters are provided that aid classification into categories of large merchant ship type 1, large merchant ship type 2, large merchant ship type 3, medium merchant ship, oiler, car carrier, cruise ship, fishing boat and fishing trawler. The proposed analysis and classification techniques were assessed through trials with 877 real acoustic signatures recorded under varying conditions of ship’s speed and sea state. The classification trials revealed a high accuracy of 94.7%.

From individual responses to population effects: Integrating a decade of multidisciplinary research on blue whales and sonar

AbstractAs ecosystems transform under climate change and expanding human activities, multidisciplinary integration of empirical research, conceptual frameworks and modelling methods is required to predict, monitor and manage the cascading effects on wildlife populations. For example, exposure to anthropogenic noise can lead to changes in the behaviour and physiology of individual marine mammals, but management is complicated by uncertainties on the long‐term effects at a population level. We build on a decade of diverse efforts to demonstrate the strengths of integrating research on multiple stressors for assessing population‐level effects. Using the case study of blue whales exposed to military sonar in the eastern north Pacific, we model how behavioural responses and environmental effects induced by climate change affect female survival and reproductive success. Environmental changes were predicted to severely affect vital rates, while the current regime of sonar activities was not. Simulated disturbance had a stronger effect on reproductive success than adult survival, as predicted by life‐history theory. We show that information on prey resources is critical for robust predictions, as are data on baseline behavioural patterns, energy budgets, body condition and contextual responses to noise. These results will support effective management of the interactions between sonar operations and blue whales in the study area, while providing pragmatic guidance for future data collection to reduce key uncertainties. Our study provides important lessons for the successful integration of multidisciplinary research to inform the assessment of the effects of noise and other anthropogenic stressors on marine predator populations in the context of a changing environment.

Acoustic multipath structure in direct zone of deep water and bearing estimation of tow ship noise of towed line array

In the towed line array sonar system, the tow ship noise is the main factor that affects the sonar performance. Conventional noise cancelling methods assume that the noise is towards the endfire direction of the array. An acoustic experiment employing a towed line array is conducted in the western Pacific Ocean, and a strange bearing-splitting phenomenon of the tow ship noise is observed in the array. The tow ship noise is split into multiple noise signals whose bearings are distributed between 10° and 90° deviating from the endfire direction. The multiple interferences increase the difficulty in recognizing the target for the sonar operator and noise cancellation. Therefore, making the mechanism clear and putting forward the tow ship noise splitting bearing estimation method are imperative. In this paper, the acoustic multi-path structure of the tow ship in deep water is analyzed. Then it is pointed out that the bearing-splitting phenomenon is caused by the main lobe of direct rays and bottom-reflected rays, as well as several side lobes of direct rays. Meanwhile, the indistinguishability between the elevation angle and the bearing angle due to the axial symmetry of a strict horizontal line array causes the bearing to deviate from the endfire direction. Based on the theory above, a method of estimating bearing of the tow ship noise in deep water is proposed. The theoretical analysis results accord with the experimental results, which helps to identify the target and provide correct initial bearing guidance for noise cancelation methods.

Recalibrating the Department of National Defence approach to active sonar impact management

Fielding a well-trained, combat-ready military, and observing Canada's responsibility as environmental stewards are at times conflicting priorities for the Department of National Defence (DND). As new low frequency sources are introduced into service, DND must review and update policies and procedures regarding the use of active sonar to minimize its impacts on marine mammals with an evidence-based approach. Risk is mitigated primarily through avoidance, which requires an understanding of marine mammal distribution in order to avoid the most sensitive species and their habitats. In parallel, a research and development program evaluates and develops technological solutions to minimize the risk of harm. By first embracing an empirical framework to assess acute and chronic impacts, DND has been able to partner with other government departments and researchers to develop technology targeted towards the residual risks to marine mammals posed by sonar operations.

Target localization in range and depth from a passive linear towed array

Stealthily estimating the range and depth of a sound source from a submarine passive sonar is of capital tactical importance: it allows to adopt the appropriate escape or attack strategy towards the threat. The main difficulty in passive localization is that the transmitted waveform is unknown. Nevertheless, array processing can reveal some features of the acoustic field allowing localization, such as the arrival angles of the multiple paths, and the time delays between them. In this paper, we have compared two methods. The first one is a direct method, which simulates the field features from a set of possible source locations, and compares them to the measured features. The second one, an indirect method, uses the measured features to backpropagate the acoustic field. The former method has already been tested on simulated and real data in (Bennaceur et al., in OCEANS’18 MTS/IEEE, Charleston) and (Bennaceur et al., in OCEANS 2021, Singapore-U.S. Gulf Coast). Here, both methods are applied on real signals from a linear towed array, using bearings only and both bearings and time delays. The main advantage of these methods is that they are automatic in the sense that no sonar operator is required to estimate the source location.

Design of Host Computer for sonar based on Ethernet

The Host Computer for sonar based on Ethernet is suitable for Single-beam Mechanical Scanning Sonar with a scanning frequency of 675KHZ and 1MHz. It is completed by using C# programming language in Windows. The Host Computer based on the framework of C#.Net. TCP, UDP are used to communicate between single beam mechanical scanning sonar and Host Computer. The scan data of sonar can be received by the Host Computer, the functions of setting and query sonar operation parameters are provided. The Host Computer uses two network transport layer protocols, TCP and UDP. Most of the sonar data transmission uses only a single protocol. Finally, the received sonar scanning data will be displayed. Wireshark is used to verify the packet capturing of the transmission process.

In Situ Measurement of Sound Attenuation by Fish Schools (Japanese Horse Mackerel, Trachurus japonicus) at Mid-Frequency Bands

Acoustic waves are attenuated by fish schools as they propagate through the ocean. The attenuation by fish schools is not currently considered in fishery acoustics and sonar applications, especially at mid-frequency bands. In this study, fish school attenuation experiments were conducted with a number of individual fish in situ in a net cage at mid-frequency bands (3–7 kHz). The target fish species was the Japanese horse mackerel (Trachurus japonicus), which typically forms fish schools in the coastal ocean of northeastern Asia. The attenuated acoustic waves were measured for the cases of non-net, only net (0), 100, 200, 300, 400, and 500 individual horse mackerels in the net cage. Results showed that the acoustic signal attenuation increased with the number of horse mackerels. The mean and maximum attenuation coefficients were approximately 6.0–15.4 dB/m and 6.5–21.8 dB/m for all frequencies, respectively. The measured attenuation coefficients were compared with the ones from previous studies to propose new regression models with normalized extinction cross-sections of weight and length of fish. This study confirmed that the fish school attenuation could not be ignored and compensated at mid-frequencies in the ocean. These results would be useful for fishery acoustics, especially in the development of scientific echo-sounder, and naval applications of sonar operations and analysis.

Operation of a multi-frequency parametric sidescan sonar in an ice waveguide

Hydroacoustic systems for mineral exploration, solving engineering problems and monitoring the ecological state of the world’s oceans are currently being intensively developed. However, the practical use of hydroacoustic systems for solving the problems under consideration, operating in the traditional mode, has some significant limitations. These restrictions are largely related to the state of the marine areas in which such work is carried out. Especially little is known about the patterns of propagation and interaction of acoustic waves in marine basins with ice cover. These areas are rich in minerals and intensive shipping is developing in them. Therefore, an important place in acoustic research is occupied by the study of the acoustic properties of the ice cover of the polar regions of the Earth. This is determined by the fact that the ice sheet is a unique constantly collapsing and renewable natural system. In this regard, the conditions for the propagation of acoustic waves are changing. More than 70% of the Arctic basin is covered with ice, the lower boundary of which has significant irregularities with a standard deviation of up to 3 m, so the scattering of acoustic waves at such a boundary is significant and different at different frequencies. The formation of the acoustic field of the hydroacoustic systems used in these conditions is quite complex. Therefore, the task of assessing changes in the characteristics of the field and the use of appropriate hydroacoustic systems for their effective use is urgent.

Returning to periscope depth in a circular control room configuration

Previous research has shown that co-location of operators dependent on each other for task-relevant information can relieve the previously identified bottleneck of information between the Sonar Control (SOC) and Operations Officer (OPSO) in submarine sound and control rooms. This research aimed to examine the impact of a novel inwards facing circular configuration on communications and tasks within a co-located control room. Ten teams participated in high and low demand Return to Periscope Depth scenarios in a simulated submarine control room. All communications between operators were recorded and compared with a baseline study of contemporary operation. The findings show that the novel circular configuration led to a reduction in communications, but an increase in information shared. Indicating that the teams had become more efficient at passing relevant information. Furthermore, teams were able to complete a greater number of tasks.

Complex of morphological analysis of images for side-scan sonar on basis of Griffon hardware platform

The article describes the developed computational complex of morphological image analysis for interferometric side-scan sonars. Examples of modeling the operation of the side-scan sonar in cases where the structure of bottom sediments is not known in advance are given. The results of the analysis of experimental data are considered. A description is given of the structure for constructing algorithms for morphological image analysis used in the operation of the complex. Recommendations are given on the effective use of the computing capabilities of the Griffon hardware platform for organizing parallel-conveyor data processing of side-scan interference sonars in real time. Hardware and software modeling has been performed to assess the most important performance characteristics of a computing complex and determine the optimal organization of data processing. The obtained results of processing sonar images streams showed that the use of the Griffon hardware platform provides advantages in terms of organizing the computing process, including parallel operation of different sections of the computing pipeline, reducing the load on the transport bus and pipeline delay, as well as reducing the load and freeing up CPU resources for morphological processing.

Knowledge representation in goal-motivated decision making for intelligent active sonar

We consider knowledge representation in a goal-driven model for intelligent, or cognitive, active sonar designed to improve system performance and reduce sonar operator burden. The intelligent sonar uses observations of the environment (broadly defined) and predictions of future behavior to make decisions that leverage system resources to address a set of goals. These goals are created and managed by the system itself with possible input from the sonar operator. Decisions take the form of action selections, where actions may include transmit waveform, ping interval, illuminated region, etc. Each action is assigned a utility score, or metric, based on how well it addresses each system goal, with weighting according to how simultaneous (possibly competing) goals are prioritized by the system and/or operator. Metrics for evaluating candidate actions rely on signal processing models for localization and tracking in active sonar. To illustrate the impact of the structure of utility metrics on system behavior, we study the performance and decision-making behavior of the intelligent sonar system in simulated scenarios in which the system is tasked with addressing multiple search and track goals simultaneously. The effect of the chosen metric structure on the impact of goal prioritization and operator input is explored and discussed.

Side-Scan Sonar Image Synthesis Based on Generative Adversarial Network for Images in Multiple Frequencies

The side-scan sonar (SSS) is a critical sensor device used to explore underwater environments in the deep sea. Gathering SSS data, however, is an expensive and time-consuming task because it requires sensor towing and involves complicated field operations. Recently, deep learning has been making advances rapidly in the field of computer vision. Benefiting from this development, generative adversarial networks (GANs) have been demonstrated to produce realistic synthetic data of various types, including images and acoustics signals. In this letter, we propose a GAN-based semantic image synthesis model based on GAN that can generate high-quality SSS images at a low cost in less time. We evaluate the proposed model using both shallow and deep water SSS data sets that include a diverse range of imaging conditions. such as high and low sonar operating frequencies and different landscapes. The experimental results show that the proposed method can effectively generate synthesized SSS data characterized by the shape and style of real data, thereby demonstrating its promising potential for SSS data augmentation in diverse SSS relevant machine learning tasks.