Sonar Applications Research Articles

Graduate programs in acoustics in the Physics and Oceanography Departments at the Naval Postgraduate School

The Departments of Physics and Oceanography at the Naval Postgraduate School offer graduate programs leading to MS and PhD degrees. The acoustic track within the Department of Physics offers degrees in applied physics and engineering acoustics. Engineering acoustics degrees can be completed in either resident or distance learning modes. The department also offers stand-alone academic certificate programs in fundamentals of engineering acoustics, underwater acoustics, and sonar system applications, with a set of three certificates leading to a MS degree in engineering acoustics. MS and PhD programs are interdisciplinary, with coursework, traditional lectures, and laboratory exercises drawn primarily from the fields of physics, electrical engineering, and fundamental acoustics. The Oceanography Department offers degrees in both Physical Oceanography and a joint degree with Meteorology that are interdisciplinary. Both oceanography degrees contain a fundamentals of ocean acoustics course which is tightly coupled to the curriculum through dispersion relations. Connections between ocean processes and detection theory are made through a course on tactical oceanography. Subjects covered in both departments include fundamentals of acoustics; the generation, propagation, and reception of sound in the ocean; acoustic signal processing; and detection fundamentals.

Nonlinear classification and regression in underwater sonar applications using convex-complex neural networks

Recently, the sonar signal processing community has seen increased interest in neural networks for nonlinear classification and regression. While innovative neural network architectures have achieved remarkable results in other fields such as computer vision and natural language processing, their application to sonar data presents challenges. The multiple nonlinearities in these networks make it difficult to interpret results. Recent works such as Grad-CAM and Layer-wise Relevance Propagation attempt to empirically visualize what features are being selected by the network, but there has been little theoretical work to explain why neural networks perform so well. However, recent work by Pilanci et al. has shown that two-layer neural networks with a ReLU activation function can be reformulated as a convex optimization that can be solved optimally in polynomial time. Their results rely on the observation that ReLU neural networks are piece-wise linear models that lift the original data into a higher dimensional feature space. Using a valid activation function, we show that this Convex Neural Network framework can be extended to applications where the input data is complex-valued. We evaluate our algorithm on simulated sonar data. [This talk will present research funded by DoD Navy (NEEC) Grant number N001742010016 and the ONR grant numbers N000142112420 and N000142312503.]

A High Performance Parallel Approach to Delay Sum Beamformer in a Homogeneous Multicore Environment

A Cache-Aware Beamformer (CABF) algorithm for the DAS beamformer in a homogeneous multicore processor environment is presented. The context of the proposed algorithm is established by discussing the case for a refined multicore implementation of the beamformer algorithm for a sonar application. The algorithm is designed, implemented, and compared to a regular pthread multicore implementation and a standard OpenMP-based implementation, using arithmetic intensity as the metric. FMA implementations of the algorithms are carried out, and the CABF algorithm is shown to achieve a better arithmetic intensity. A 6000-element array is designed with a simultaneous forming of 200 beams to test the efficacy of cabf in a multicore platform. The results show a 73 % increase in GFLOPS for FMA operations. The performance of the beamformer algorithm for different data sizes is studied, and on average, a 36 % improvement in computational performance is achieved compared to the OpenMP-based implementation.

DNTFE-Net: Distant Neighboring-Temporal Feature Enhancement Network for side scan sonar small object detection

Side-scan sonar small target detection becomes fundamental work for side-scan sonar applications which holds vital importance for marine engineering, maritime military, and so on. However, existing algorithms suffer from performance degradation and poor portability when sonar data is limited. We find that benefit from the strong temporal correlation of sonar images, background information from the long-term images and foreground information from the short-term images can be used to enhance small targets in the current image. To fully utilize these two cues, this paper presents the Distant Neighboring-Temporal Feature Enhancement Network (DNTFE-Net) for detecting small objects in sonar images. The algorithm is based on the signal acquisition process of sonar devices, which is considered a continuous time sequence. Firstly, a flexible foreground feature alignment module is developed to align the foreground information between neighboring signals and the current signal so that foreground information can be used to enhance small target features. Then, we propose a background comparison module for selecting distant-temporal signals that provide the most contextual information in order to improve the ability of migrating to different domains. Finally, a foreground background feature amalgamation module is designed to fuse neighboring and distant-temporal signals for enhancing small target features. Furthermore, real sonar data is collected to compose the real continuous sonar dataset. Our DNTFE-Net exhibits superior recognition performance on the real sonar dataset compared to other popular methods, with an improvement of at least 27%. Our algorithms are publicly available at https://github.com/zbyhnu/DNTFE-Net.git.

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.

Metric and Upper Dimension of Extended Annihilating-Ideal Graphs

The metric dimension problem is called navigation problem due to its application to robot navigation in space. Further this concept has wide applications in motion planning, sonar and loran station, and so on. In this paper, we study certain results on the metric dimension, upper dimension and resolving number of extended annihilating-ideal graph [Formula: see text] associated to a commutative ring [Formula: see text], denoted by [Formula: see text], [Formula: see text] and [Formula: see text], respectively. Here we prove the finiteness conditions of [Formula: see text] and [Formula: see text]. In addition, we characterize [Formula: see text], [Formula: see text] and [Formula: see text] for artinian rings and the direct product of rings.

Temperature-dependent thermal buckling and free vibration behavior of smart sandwich nanoplates with auxetic core and magneto-electro-elastic face layers

This article addresses the thermomechanical thermal buckling and free vibration response of a novel smart sandwich nanoplate based on a sinusoidal higher-order shear deformation theory (SHSDT) with a stretching effect. In the proposed sandwich nanoplate, an auxetic core layer with a negative Poisson’s ratio made of Ti-6Al-4V is sandwiched between Ti-6Al-4V rim layers and magneto-electro-elastic (MEE) face layers. The MEE face layers are homogenous volumetric mixtures of cobalt ferrite (CoFe2O4) and barium titanate (BaTiO3). The mechanical and thermal material properties of the auxetic core and MEE face layers are temperature-dependent. Using Hamilton’s principle, governing equations are constructed. To characterize the size-dependent behavior of the nanoplate, governing equations are adapted with the nonlocal strain gradient theory (NSGT). By applying the principles of Navier’s technique, closed-form solutions are obtained. Parametric simulations are carried out to examine the effects of auxetic core parameters, temperature-dependent material properties, nonlocal parameters, electric, magnetic, and thermal loads on the free vibration and thermal buckling behavior of the nanoplate. According to the simulation results, it is determined that the auxetic core parameters, temperature-dependent material properties, and nonlocal factors significantly affect the thermomechanical behavior of the nanoplate. The outcomes of this investigation are expected to contribute to the advancement of smart nano-electromechanical systems, transducers, and nanosensors characterized by lightweight, exceptional structural integrity and temperature sensitivity. Also, the auxetic core with a negative Poisson’s ratio provides a metamaterial feature, and thanks to this feature, the proposed model has the potential to be used as an invisibility technology in sonar and radar-hiding applications.

Hearing your voice in the crowd: How do bats echolocate in dense swarms?

In order to navigate their environment and find food, bats rely on comparing returning echoes to their broadcast signal. Through decades of research, we have a good understanding of how single bats accomplish this task, but we still don’t know how bats in dense groups can echolocate without interference from others. A spectral Jamming Avoidance Response (JAR) has long been proposed as one strategy, in which bats flying together shift frequencies to avoid interference. Recent work, however, questions this strategy, and suggests bats may not use any JAR for sensing in groups. Bats have long served as bioinspiration for active sensing devices, so understanding strategies for collective sensing in bats has implications for applications of synthetic sonar. In this talk, I will review the “hot topic” of bat biosonar in swarms and show new data from our research that suggests spectro-temporal variation may play a role for sensing in even the largest, densest bat swarms.

Optimizing hydrophone-preamplifier systems: Theoretical design, computational analysis, and experimental validation for towed array applications

Hydrophone response parameters greatly influence the design of pre-amplifier and analog to digital conversion (ADC) components. Here, we show a comprehensive methodology for the modeling of hydrophone response, focusing on the theoretical, computational, and experimental approach for low and high frequency SONAR applications. Theoretical calculation of hydrophone response based on electromechanical properties is compared with Finite Element Analysis (FEA) and experimental measurement. An electrical equivalent hydrophone simulator Butterworth-Van Dyke (BVD) circuit is constructed based on admittance and susceptance measurements to represent the transducer's electrical behavior for testing pre-amplifier and ADC response without the presence of environmental or hydrophone noise. This model facilitates a systematic approach to towed array front-end design with hardware in the loop to update models to improve performance prediction. Measurements of the constructed transducer system validate the theoretical and computational models, providing insights into real-world performance.

Bistatic spatial coherence for micronavigation of a volumetric synthetic aperture sonar

Combining multiple passes of a volumetric synthetic aperture sonar (SAS) may open new possibilities for sonar imaging, if the passes can be precisely aligned. Previous work suggests that coherence-based micronavigation is a potential approach to achieve this alignment. However, existing coherence-based techniques require assumptions that break down in many volumetric imaging geometries. This breakdown leads to a loss of coherence that has not been widely studied, and consequently, has hindered the application of micronavigation to volumetric SAS systems. This work will explore the impact of sensing range and bistatic separation on spatial coherence in downward-looking, volumetric sonar applications where the far-field assumption no longer holds. As signals transmitted from downward-looking sonars may penetrate into the sediment floor, this analysis will consider both interface and volumetric scattering. First, using both simulated and experimental sonar data, we will quantify the loss in coherence that occurs in these near-field geometries. Then, methods to regain lost coherence will be examined in an effort to enable spatial coherence-based navigation for a volumetric SAS. Finally, the work’s relevancy for ongoing efforts in acoustic navigation and repeat-pass imaging will be discussed.

Discovery and learning of feature geometry in underwater acoustic sensing: Case studies and challenges in active sonar applications

Interpretable artificial intelligence (AI) and related machine learning (ML) techniques are gaining popularity for underwater acoustic sensing applications. However, interpretability of machine learnt features poses a fundamental challenge to successful application of these powerful data science techniques to underwater acoustics. Autonomous sonar target recognition is especially interesting from the data science perspective as beyond target-specific features, a sonar ping response typically includes significant interference from the environment, e.g., clutter, multipath scattering, etc. Such environmental interference, resulting from complex, dynamic, unpredictable, and often unknown factors, manifest as identifiable structures in acoustic color or alternate multi-dimensional feature representations that can lead to machine learning and classification errors. In this talk, we will discuss these challenges commonly encountered in underwater acoustics using case studies from field experiments in active sonar target recognition. Some results will also include physics-driven simulations in this domain to provide robust ground truths. In particular, we will posit how braided feature geometry and its representation, as well geometry of overlap between features can render a sonar target feature informative, explainable, and discoverable. [Work funded by the ONR Grant Nos. N000142112420 and N000142312503 and DoD Navy (NEEC) Grant No. N001742010016.]

The applications of sonar and radar systems in underwater detection

The development of sonar and radar systems utilizing sound and radio waves, respectively, has evolved significantly over the past two centuries. Recent decades have witnessed remarkable progress in remote sensing using high-frequency Doppler radars for marine surface parameter analysis. Understanding animal behavior around sustainable ocean energy sources is crucial for mitigating potential risks linked to such installations. Increasing the Signal-to-Noise Ratio (SNR) enhances signal reception. A technique estimating noise in the detection coil's first half determines the transfer function between the reference and detection coils. This enables separation of the Ultra-Nuclear Magnetic Resonance signal from noise, preserving it using non-linear fitting. Sonar systems, using sound waves, detect underwater objects, map surfaces, and track marine life. Sound waves emitted by the transmitter are reflected back to calculate distances. Challenges in sonar systems include a blind zone caused by submerged transducers in shallow water. To improve, SNR enhancement is critical. Sonar's future may rely on AI-assisted signal processing. Radar systems, operating with radio waves, share features with sonar, focusing on object detection and ranging. Improvement avenues include 3D imaging and AI-enhanced signal processing. In conclusion, sonar and radar systems are crucial tools for underwater exploration and object detection. While challenges persist, innovative solutions and AI advancements promise enhanced capabilities for both systems.

Applications and analytical approaches using imaging sonar for quantifying behavioural interactions among aquatic organisms and their environment

Abstract For many aquatic animals, distributional patterns, trophodynamic relationships, and reproductive activity are driven by complex biotic and abiotic ecosystem interactions that influence behaviour. Linking behavioural information to environmental stimuli and stressors can, therefore, help to anticipate population and ecosystem responses to changing conditions and inform management. However, behavioural information is challenging to obtain because many sampling gears do not provide adequate spatial or temporal resolution, or potentially alter behaviours. Traditionally, most behavioural studies have been laboratory experiments, while behaviour in situ has often been inferred indirectly. Advancements in imaging sonar technology enable the study of in situ behaviours with the potential to address many understudied relationships. In this review we discuss applications of imaging sonar among a meta-analysis of 155 studies of aquatic organisms in their environments. We evaluate the performance of imaging sonar for studying inter- and intra-specific interactions, associations with complex and sensitive habitats and low-visibility environments, and to evaluate traditional fisheries sampling gears. We examine the data processing and analytical methods used to refine taxonomic resolution, manage time use and autocorrelation through sub-sampling, extract behavioural metrics applied to ecological processes, and for automating abundance estimates and image classification with the goal of providing a resource for researchers.

Utilizing gammatone filter coefficient to improve human mouth-click signal detection using a multi-phase correlation process

This study introduces an enhanced structured signal processing scheme for detecting the “transmit-echo” of mouth-click signals used by blind individuals for echolocation. The processing scheme is based on coexisting work on the detection of the mouth-click signal, with an additional band-pass filter process introduced before synthesis, multi-phase correlation, and summation. The level of side lobes at the output was improved by more than −19 dB, and the number of local maxima was minimized by using a band-pass filter. The detection of “transmit-echo” results using artificial mouth-click signal data was validated and achieved a 100% success rate in detecting obstacles at 60 cm, 80 cm, and 100 cm. Nonetheless, the detection scheme discussed in this investigation is thought to be intuitive, having been learned from the human hearing process. The emerging concepts in this research are expected to benefit radar and sonar system applications in the near future.

Numerical modeling of geometrically nonlinear responses of smart magneto-electro-elastic functionally graded double curved shallow shells based on improved FSDT

The use of functionally graded magneto-electro-elastic (FG-MEE) shells in smart composite structures like sensors, transducers, vibration monitoring, aeronautics, shape tracking, medical and sonar applications has a lot of promise. The accurate modeling approach for situations with several physics linked is quite difficult. In this paper, an improved first-order shear deformation hypothesis is used to inspect the magneto-electro-elastic coupling performance of thin-walled smart structures with laminate design presenting FGM composite as inactive material and using the magneto-piezoelectric patches as combined sensor and actuator features. The govern equations of motion, electric and magnetic distributions through the thickness direction of shell element are derived from the virtual of Hamilton principle. The simulations are carried out using a self-coded FE software. A numerical analysis of the nonlinear deflection of FG-MEE shells is performed under multiphysics loading. To ensure that the current MEE formulation is accurate, the findings were compared to existing solutions from the literature, and remarkable agreement was identified. A parametric analysis is conducted to demonstrate how the material composition affects the deflection throughout the thickness. Further, new numerical results, physical insights and finding have been provided in the present manuscript taking into account influences of several important parameters such as material graduation index and various boundary conditions. The novel MEE FE model suggested in this study is highly useful for the accurate development and design of FG-MEE structures.

Aural based scene understanding for sonar applications

Acoustic returns scattered from either surfaces or objects are typically interpreted using image processing techniques such as beamforming and computer vision models such as convolutional neural networks. However, image representations can be burdensome to generate, creating delayed responses in real-time scenarios, and difficult to parse from the scene-understanding perspective. This work investigates direct exploitation of the information content from streaming, near-raw sonar time series data for the purpose of scene characterization. Summary statistics drawn from the sonar performance estimation community and communities that study aural-based perception are used to identify scene characteristics such as flat sand, ripples, gravel beds, or sea grass, in side-looking synthetic aperture sonar (SAS) data. An empirical multi-class classification study is presented that explores the performance of seafloor characterization methods on time series versus imagery.

Forward looking sonar applications for unmanned vehicles

Unmanned vehicles have the potential to greatly expand our ability to observe dynamic ocean environments. Whereas distributed acoustic sensors can monitor discrete measurement points in time and space, autonomous maritime platforms provide increased flexibility. Acoustic principles are a key component of this functionality and forward looking sonar (FLS) systems can acquire acoustically derived data products while enabling better informed autonomy behaviors based on enhanced situational and environmental awareness. This paper explores applications of forward look sonar technology when integrated with underwater vehicles. Utility of both interferometric and two-dimensional acoustic data products are examined to illustrate the impact on technologies associated with navigational drift mitigation and ocean observation. Results from sonar platform integration and in-water acquisition for development of an underwater terrain-aided navigation (UTAN) particle filter, an acoustic enabled simultaneous localization and mapping (SLAM) algorithm, obstacle avoidance techniques, and seabed observations are presented.

Ethernet Over Coaxial (EoC) Cable Telemetry Over High Voltage Dc And High Power Ac For Airborne Sonar Applications


 
 
 We have proposed a new passive component-based coupling scheme to simultaneously transmit high power AC, high voltage DC and high speed data through a long, single core coaxial cable, particularly for Airborne Sonar applications. This method is intended to replace the bulky multicore cables with single core coaxial cables that are compact and effective for transmitting data over long distances. This coupling scheme consists of three couplers to superimpose high power AC, high voltage DC and high speed data at the onboard end of the cable, and three decouplers to separate them at the remote end. The long cable, couplers, decouplers and the acoustic transducer are represented by their corresponding equivalent circuits that are cascaded together to construct the complete network. Power loss in the circuit is minimised by providing impedance matching networks in the form of a T-network transmission line and a tuning coil. The Ethernet over Coaxial (EoC) module can transmit high speed Ethernet data at the rate of 10/100 Mbps for telemetry from onboard to remote units and vice versa. DC voltages up to 300 V are coupled to the single core coaxial cable from the onboard unit and superimposed with high power AC signals and high-speed data. Passive filter-based coupling and decoupling schemes are demonstrated. Network simulation studies and experimental studies are carried out to verify and validate the equivalent circuit model. The transfer functions of each set of couplers and decouplers are determined independently as well as collectively and their effects on the underwater acoustic performance of the system are studied. The model resuls are found to agree with experiments. The proposed system is capable of generating an acoustic Source Level of about 195 dB with the supply of 125 V continuous wave (CW) AC signals, with simultaneous transmission of data at 10 Mbps and DC supply of 260 V from the on-board unit to the remote unit at the end of a coaxial cable of 200 m length. Introduction of impedance matching network is found to increase the source level by about 12 dB.
 
 

Design optimization of an optical fiber air-backed concentric hybrid mandrel hydrophone by an iterative design and finite element analysis for submarine passive array applications

The optical fiber hydrophones are well suited for the submarine passive sonar applications taking into consideration of their high sensitivity, fully electrically passive characteristics etc. and are definitely an equivalent replacement for Lead Zirconate and Lead Titanate ceramic hydrophones. In this paper, finite element analysis of several sets of cases of concentric hybrid material-based air-backed OF mandrel hydrophones are analysed and tuned to their geometric dimensions and their mechanical properties for design optimization, suitable for submarine sonar applications. A finite element method is adopted for analysis, strain and sensitivity calculations using ANSYS software. An inverse dependency of the sensitivity and the natural frequency were observed and was overcome by an innovative method where a hybrid inner hollow tube, combining both Aluminium and high-density polyethylene materials, were used, improving both the stiffness property and compliance of the mandrel and thereby improving both the natural frequency and pressure sensitivity. The harmonic analysis was carried out at 5 KHz acoustic pressure, by adding hydrostatic pressures of 2 MPa, 3 MPa and 4 MPa and obtained satisfying results. The acoustic directivity of this OF mandrel hydrophone was estimated, plotted and an omni directional pattern was obtained.

An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

Rapid developments in synthetic aperture (SA) systems, which generate a larger aperture with greater angular resolution than is inherently possible from the physical dimensions of a single sensor alone, are leading to novel research avenues in several signal processing applications. The SAs may either use a mechanical positioner to move an antenna through space or deploy a distributed network of sensors. With the advent of new hardware technologies, the SAs tend to be denser nowadays. The recent opening of higher frequency bands has led to wide SA bandwidths. In general, new techniques and setups are required to harness the potential of wide SAs in space and bandwidth. Herein, we provide a brief overview of emerging signal processing trends in such spatially and spectrally wideband SA systems. This guide is intended to aid newcomers in navigating the most critical issues in SA analysis and further supports the development of new theories in the field. In particular, we cover the theoretical framework and practical underpinnings of wideband SA radar, channel sounding, sonar, radiometry, and optical applications. Apart from the classical SA applications, we also discuss the quantum electric-field-sensing probes in SAs that are currently undergoing active research but remain at nascent stages of development.