Sonar Research Articles

Weak fluctuating spectral line reconstruction using deep learning

The detection of weak fluctuating spectral lines emitted by underwater and surface vehicles poses a challenging problem for passive sonar system. Therefore, a spectral line reconstruction algorithm based on deep learning called the DEDAN, is proposed. The DEDAN learns the time-frequency correlation of spectral lines through end-to-end training and then reconstructs the spatial location of spectral lines. Simulation results show that the DEDAN is robust to ambient noise, and outperforms other reconstruction algorithms at a mixed signal-to-noise ratio as low as -22 dB to -26 dB. Its reconstruction performance is also verified by the measured South China Sea data.

Temporal change of seafloor scattering and its dependence on environmental parameters in shallow-water sandy sites

In the ocean, the performance of active sonar systems used for object detection and seafloor characterization can be affected when the acoustic properties of the seafloor change due to near-bottom hydrodynamics and biological activity. Determining the dominant environmental mechanisms and corresponding time scales that regulate seafloor scattering will increase our understanding of the performance of these remote-sensing applications. To this end, a high-frequency active acoustic system (operating at 38 kHz, 70 kHz and 200 kHz), a wave-sensing CTD, and a stereo camera were deployed on the seafloor in a series of experiments lasting from two weeks to five months. Seafloor scattering measurements were obtained in two shallow water locations in New Hampshire, USA: a wave-dominated site and a tidal current dominated site. Daily and weekly trends in mean scattered levels and the mechanisms causing their temporal variability are discussed. The temporal change in scattering as a function of angle is compared to the small-slope approximation model where seafloor roughness estimates were obtained using stereophotogrammetry.

Expendable Conductivity–Temperature–Depth-Assisted Fast Underwater Sound Speed Estimation by Convolutional Neural Network with Reduced Fully Connected Layers

Obtaining accurate sound speed profiles (SSPs) in near-real-time is of great significance for ocean exploration, underwater communication and improving the performance of sonar systems. In response to the problem that traditional sound speed estimation methods cannot obtain real-time sound speed distribution or rely too much on sonar observation data, we propose an SSP estimation method based on a convolutional neural network with reduced fully connected layers (RFC-CNN) in this paper. This method utilizes neural networks to extract the complex nonlinear features of various types of data. With the help of the historical SSPs and shallow seawater sound speed and temperature data obtained by expendable conductivity–temperature–depth probes (XCTDs), a more accurate estimation of the regional sound speed distribution can be realized quickly. This approach can save the observation cost and significantly improve the real-time performance of SSP estimation.

Underwater Object Prediction Using Sonar Waves

The detection and differentiation of potentially hazardous objects, such as mines and rocks, in underwater environments are crucial for the safe navigation of submarines during warfare scenarios. This study compares and contrasts different machine learning algorithms to determine whether an object detected by a submarine's sonar system is a mine or a rock. From the comparative analysis, we have proposed the Logistic Regression Model (LRM), which is used to train and test the models. To extract pertinent features like signal intensity, frequency content, and time-domain characteristics, sonar signals are essential to the labeled dataset. This dataset is made up of sonar signals and corresponding object classifications (mine or rock) based on the frequency range. These features served as inputs to the machine learning algorithms and enabled the features to learn the underlying patterns and make accurate predictions. In this paper, we have contributed a novel approach to underwater object detection using LRM, which is applied to sonar data. In summary, this research presents an innovative solution to the challenge of underwater object detection using sonar signals and machine learning algorithms. The developed model exhibits promising results, opening up new possibilities for advancing our understanding of underwater environments and enhancing underwater exploration and security capabilities.

A bat biomimetic model for scenario recognition using echo Doppler information

The flying bat can detect the difference in Doppler frequency between its echolocation transmission signal and the echoes in its surroundings, enabling it to distinguish between various scenarios effectively. By examining the bio-sonar biomimetic model of a flying bat that uses echo Doppler information for environmental recognition, it may enhance the scene recognition capability of human ultrasound sonar during movement. The paper establishes a three-dimensional clutter model of the flying state of bat bio-sonar for bats emitting constant frequency signals. It proposes a scene recognition method that combines multi-scale time-frequency feature analysis with a convolutional neural network (CNN). The short-time Fourier transform of different scales extract the Doppler and range dimensions, which are then fused to create a multi-scale feature plane containing both Doppler and range information. Combined with CNN’s powerful image classification and recognition capabilities, extract features from multi-scale feature planes of different clutter scenes to achieve environment recognition based on the differences in Doppler and range dimensions of echoes in various directions. Through computer simulations, this study provides a numerical interpretation of the environmental classification and perception capabilities of bats in flight. The algorithm significantly improves scenario classification and recognition performance according to simulation results, with accuracy exceeding 98% in varied clutter scenarios at 30 dB signal noise ratio. Based on computer simulations, an experimental scene was constructed and actual echo signals were collected and analyzed. The experiments demonstrate that utilizing Doppler information enables the classification and recognition of cluttered environments. The effectiveness of the proposed algorithm was also verified. Ultrasonic sonar systems, such as navigation robots and helicopter obstacle avoidance, can apply this biomimetic model and algorithm for environmental recognition during motion.

Multi-frame coherent track-before-detect method for weak tones in passive sonar

To address the performance degradation caused by power fluctuations of tones, the multi-frame coherent integration-based track-before-detect (TBD) method is proposed. In the proposed method, the high gain advantage of coherent integration and the information accumulation ability of multi-frame TBD method based on batch processing technique are combined to improve the detection and tracking performance of fluctuating tones. Firstly, a new measurement model is established with batch processing technology and coherent integration across multiple data frames is achieved utilizing the tonal coherence, thus the SNR in measurement is greatly improved. Then, a new likelihood ratio function is derived to match the established measurement model, ensuring that TBD processing can be performed correctly. Finally, the effects of the newly established likelihood ratio and the batch length on the detection performance are analyzed in detail through theoretical derivations. The superiorities and robustness of the proposed method are demonstrated through simulation analysis and processing results for sea experimental data.

Design of acoustic coating for underwater stealth in low-frequency ranges

In this study, it is aimed to protect underwater vehicles against active sonar systems by anechoic coating. A matrix material with close acoustic impedance to water, resistivity to hydrostatic pressure, suitability for the marine environment, and high material loss factor is selected. At low frequencies, the inclusions in different shapes and sizes are added to the matrix material. Since solid inclusions will increase the coating mass considerably, air cavities are preferred as inclusions. More attention is paid to low-frequency absorption, especially below 1 kHz, because of advancing sonar technology. The acoustic performance of the designed models is compared in three frequency ranges: low (below 3 kHz), middle (3–6 kHz), and high (6–10 kHz). The designed models are constructed by considering hydrostatic pressure; hence, volume of air cavities is tried to decrease, while absorption performance is aimed to increase. Therefore, a conical cavity which commonly used in the literature is optimized by chancing its dimensions and location. Also, novel approaches, gong shape cavity, and sandglass cavities are introduced. The results show that, not only cavity shape, but also its location and dimensions are highly influential on absorption performance. High-volume cavities increase the absorption performance at the low-frequency range, but they are not effective at high frequencies. The gong shape and sandglass air cavities show broadband absorption; also, gong shape cavity volume is less than literature models. Thus, its usability increases at deep waters. The results of this study provide novel underwater acoustic coating models for various applications.

A High-Resolution Imaging Method for Multiple-Input Multiple-Output Sonar Based on Deterministic Compressed Sensing.

Differences between conventional sonar and Multiple-Input Multiple-Output (MIMO) sonar systems arise in achieving high angular and range resolution. MIMO sonar uses Matched Filtering (MF) with well-correlated transmitted signals to enhance spatial resolution by obtaining virtual arrays. However, imperfect correlation characteristics yield high sidelobe values, which hinder accurate target localization in underwater imagery. To address this, a Compressed Sensing (CS) method is proposed by reconstructing echo signals to suppress correlation noise between orthogonal waveforms. A shifted dictionary matrix and a deterministic Discrete Fourier Transform (DFT) measurement matrix are used to multiply received echo signals to yield compressed measurements. A sparse recovery algorithm is applied to optimize signal reconstruction before joint transmit-receive beamforming forms a 2D sonar image in the angle-range domain. Numerical simulations and lake experimental results confirm the effectiveness of the proposed method, by obtaining a lower sidelobe sonar image under sub-Nyquist sampling rates as compared with other approaches.

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.

Through the sensor estimation of sound speed profiles.

A through-the-sensor method to sense the local sound speed profile (SSP) using measured acoustic wave numbers via an array of hydrophones is proposed. Ocean sounds can be treated as acoustic energy trapped as discrete modes within the water column. A Fredholm integral equation of the first kind relates the linearized (perturbative) sound speed corrections to the wave number differences between the measured values and those calculated from an acoustic kernel. Thus, a method to exploit environmental information deduced from different in situ sonar systems is proposed. Though this inversion can be unstable and non-unique, recent improvements in sparse inversions can lead to robust estimates even without an accurate starting SSP. An iterative algorithm using multiple acoustic frequencies is beneficial to achieve convergence and stability for larger sound speed corrections. This paper will compare broadband incoherent L2- and coherent L1-inversion results. Careful consideration must be made of the acoustic frequency, number of modes, a priori environmental information (e.g., water depth), and array length. The method will be first demonstrated on simulations and recordings from the Littoral Depth Discrimination Experiment 2012 (LIDDEX12) data set.

Passive Sonar Detection and Classification Based on Demon-Lofar Analysis and Neural Network Algorithms

This paper focuses on an experimental study that used passive sonar sensors as the primary information source for the submerged target in order to identify, classify, and recognize naval targets. Surface vessels and submarine generate a specific sound either by propulsion systems, auxiliary equipment or blades of their propellers, producing information known as the "acoustic signature" that is unique to each type of target. Consequently, the analysis and classification of targets depend on the processing of the frequencies produced by these vibrations (sound). utilizing the TPWS (Two-Pass-Split Windows) filter, this work aims to develop a novel technique for target identification and classification utilizing passive sonars. This technique involves processing the target's signal in the time-frequency domain. subsequently, in order to improve the frequency lines of the target noise and decrease the background noise, a TPSW algorithm is implemented in the frequency domain. By integrating narrowband and broadband analysis as inputs of an artificial intelligence model that can classify a target into one of the categories given in the training phase, the target has finally been classified. Our findings demonstrated that the suggested approach is dependent upon the size of the target noise data collection and the noise-to-effective-signal ratio.

Research on Multibeam Bathymetry Based on Geometric Analysis Models and Brute Force Search Algorithm

In recent years, multibeam sonar systems have gradually replaced single-beam sonar systems. They have become widely accepted and extensively used in the field of seabed depth detection because they overcome the lack of data between survey lines inherent to single-beam systems. The issue of high redundancy in seabed depth data has been a long-standing and challenging problem to solve. To minimize the redundancy rate of seabed depth data as much as possible, this paper conducts a study on the redundancy rate of multibeam sonar systems using different depth measurement methods based on geometric analysis and brute-force search algorithms. This paper establishes a geometric analysis model that utilizes a brute-force search algorithm to enumerate all possible outcomes and selects the optimal solution. The results, upon comparison, show that the redundancy rate of the equidistant uniform survey line method is one-sixth of that of the traditional grid measurement method, indicating good practicality and significantly improving the data space utilization of seabed depth measurement systems.

Bifunctional flexible metasurface based on graphene and vanadium dioxide for polarization conversion and absorption

A bifunctional flexible metasurface with high polarization conversion ratio (PCR) and absorptivity based on graphene and vanadium dioxide (VO2) is proposed in microwave band, which consists of metallic resonator with two VO2 films in the diagonal direction, graphene resistance film layer, two polyimide (PI) dielectric layers and copper ground plate. The proposed metasurface can be switched from a polarization converter to an absorber by employing insulator-to-metal transition in VO2, and the PCR and absorptivity are dynamically manipulated by the graphene. When the conductivity of VO2 (σvo2) is 10 S/m (room temperature) and the square resistance of graphene (Rg) is 900 Ω/sq., the metasurface becomes a broadband polarization converter with the PCR exceeding 90 % in 6.97–17.62 GHz and the corresponding relative bandwidth (RB) is 86.62 %. Once the temperature is high enough and VO2 is in its fully metallic state (σvo2 = 2 × 105 S/m and Rg = 30 Ω/sq), the metasurface efficiently absorbs normally incident electromagnetic (EM) waves in 13.33–19.47 GHz with the absorptivity exceeding 90 % (RB = 37.44 %). The influence of geometric and EM parameters and incident angles on its bifunctional features are also investigated. Moreover, the proposed metasurface exhibits polarization insensitivity under dual functions. The designed bifunctional metasurface is expected to be applied in the fields of wireless communication systems, radar and sonar systems and EM stealth technology.

Noise reduction method for ship radiated noise signal based on modified uniform phase empirical mode decomposition

Noise reduction of underwater acoustic signal is of great significance in improving signal recognition capabilities of sonar system. In this paper, as a new decomposition algorithm, modified uniform phase empirical mode decomposition (MUPEMD) is proposed to improve the calculation efficiency and alleviate mode aliasing problem. After being decomposed by MUPEMD, mode components can be classified adaptively through critical entropy value. Dynamic zero point interval filtering method based on genetic algorithm (GA-DZPIF) is proposed to filter single mode component. Structure similarity of phase diagram is established as a new evaluation index to avoid the subjective difference which can enhance the persuasiveness of experimental result. Finally, a new noise reduction method for ship radiated noise signal based on MUPEMD, GA-DZPIF and entropy cross curve is proposed. Good denoising performance has been achieved for measured ship radiated noise signal, and it has a wide range of practical application.

A new, ultra-low-cost power quality measurement technology

IEC 61000-4-30 is an excellent standard that ensures that all compliant power quality instruments, regardless of manufacturer, will produce the same results when connected to the same signal. However, instruments that comply with the Class A requirements of this standard have, until now, been too expensive for common use. Now a new set of technologies developed by an American company, in cooperation with a Japanese company, demonstrate that it is possible to manufacture three-phase power quality instruments that are fully compliant with the Class A requirements of IEC 61000-4-30 at ultra-low-cost (selling price less than 500 Euros in quantity). The development uses technologies from several fields that have not previously been related to power quality, including digital cameras, power-over-ethernet, mobile phones, and submarine sonar systems. These new technologies have been packaged in a demonstration instrument, and may be licensed to instrument manufacturers as well.

Low-Complexity Uncertainty-Set-Based Robust Adaptive Beamforming for Passive Sonar

Recent work has highlighted the potential benefits of exploiting ellipsoidal uncertainty-set-based robust Capon beamformer (RCB) techniques in passive sonar. Regrettably, the computational complexity required to form RCB weights is cubic in the number of adaptive degrees of freedom, which is often prohibitive in practice. For this reason, several low-complexity techniques for computing RCB weights, or equivalent worst case robust adaptive beamformer weights, have recently been developed. These techniques, whose complexities are only quadratic in the number of adaptive degrees of freedom, use gradient-based, reduced-dimension Krylov-subspace or Kalman-filtering methods. In this work, we review these techniques for passive sonar, analyzing their complexities and evaluating them initially on simulated data. The best performing methods are then evaluated on two in-water recorded passive sonar data sets. One set, containing a strong controlled acoustic source, demonstrates the ability of the algorithms to protect against signal cancellation when pointing at the source, and their ability to reject the source when pointing away from it. The other data set, recorded during a period when the boat was accelerating, demonstrates the ability of the algorithms to operate in the presence of speed-induced noises.

Orthogonal Chirp Division Multiplexing Based on Dictionary Theory for Integrated Sonar and Communication System

Orthogonal Chirp Division Multiplexing Based on Dictionary Theory for Integrated Sonar and Communication System

Design of Forward-Looking Sonar System for Real-Time Image Segmentation With Light Multiscale Attention Net

Design of Forward-Looking Sonar System for Real-Time Image Segmentation With Light Multiscale Attention Net

Disaster zone human and animal detection using sonar

The research introduces a novel approach to animal and human detection using sonar technology in fields such as disaster management, and under water exploration. Unlike traditional visual methods, sonar systems emit soundwaves to analyze echoes, providing unique advantages in challenging environments. The proposed method involves collecting raw sonar data, followed by preprocessing techniques for noise reduction, signal normalization, and feature extraction. Sonar’s ability to penetrate various media, including water and dense fog, makes it valuable for detecting animals and humans in low-visibility conditions. Additionally, sonar operates effectively in both day and night settings, unaffected by lighting conditions. The proposed detection system will undergo comprehensive experiments using representative datasets and real-world scenarios. Performance metrics, such as detection accuracy, precision, recall, and computational efficiency, will be analyzed and compared with existing approaches. The study showcases the effectiveness and viability of employing sonar technology for animal and human detection tasks, highlighting its unique capabilities in challenging environments.

Transfer Learning and Few-Shot Learning Based Deep Neural Network Models for Underwater Sonar Image Classification With a Few Samples

Acoustic imaging sonar systems are widely used for long-range underwater surveillance in various civilian and military applications. They provide 2-D images of underwater objects, even in turbid water conditions where optical underwater imaging systems fail. Achieving high accuracy in automatic deep learning based underwater image classification remains an open problem due to insufficient data availability, poor image resolution, low signal-to-noise ratio surroundings, etc. In this study, we conduct a comparative analysis of different advanced deep learning approaches, i.e., transfer learning and few-shot learning, to address the problem of automatic object classification in sonar images, using a few samples of data. Specifically, two metric learning-based approaches, i.e., siamese network and triplet network as well as library-based approaches, are studied under the few-shot learning paradigm. Extensive experiments are conducted on a novel custom-made dataset developed in-house, along with the publicly available SeabedObjectsKLSG dataset. In addition, the effectiveness of the sampling technique in handling class imbalance during model training is also investigated in this work. Our experimental results highlight that the few-shot learning based approach is a promising direction for future research on underwater image classification with a few samples.