Sonar Research Articles

Enhanced underwater three-dimensional imaging using acoustic orbital angular momentum waves and mode matching beamforming.

Improving the underwater three-dimensional imaging resolution of a sonar system is of great significance to achieving high-precision ocean exploration results. Actually, improving the resolution can be considered from the perspective of information acquisition. The acoustic orbital angular momentum (AOAM) wave has a modal dimension and can carry more target information. However, there are few studies on the application of AOAM waves for underwater three-dimensional imaging. This paper establishes the related signal models of the AOAM wave in underwater imaging and examines the sound field characteristics from both simulation and underwater real test data. A method called mode matching beamforming (MMBF) is proposed, and a composite structure of uniform circular array (UCA) plus spiral array is combined to study the imaging performance of AOAM waves and plane waves. The simulation results indicate that, compared with the plane wave, the sidelobe in the AOAM wave imaging beam pattern reaches -22.43 and -29.10 dB in the azimuth and elevation angles, respectively, and the mainlobe widths are reduced by 1.70° and 0.40° in both directions. Finally, this paper uses the MMBF method to process the echo data of underwater real object and realizes the imaging of the corner reflector.

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

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

Adaptive focusing for wideband beamforming in multipath environments.

This paper addresses achieving the high time-bandwidth product necessary for low signal-to-noise ratio (SNR) target detection and localization in complex multipath environments. Time bandwidth product is often limited by dynamic environments and platform maneuvers. This paper introduces data-driven wideband focusing methods for passive sonar that optimize parameterized unitary matrices to align signal subspaces across the frequency band without relying on wave propagation models which are subject to mismatch in complex multipath environments. The methods minimize the log-determinant of the wideband covariance, a measure indicative of matrix rank, ensuring the coherence of wideband data and preserving SNR. We propose two approaches: a fully adaptive method with parameters scaling directly with the number of frequency bins, and a partially adaptive method that shares parameters across frequencies to improve robustness to noise. Simulations are conducted in a shallow-water waveguide scenario to demonstrate the flexibility of data-driven focusing over traditional model-based approaches. Results from the SWellEx-96 S59 event validate our methods, showing improvement in tonal target detection and localization in the presence of strong wideband interference.

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

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

A comparison of recreational and survey-grade side-scan sonar systems in mapping reservoir fish habitat

Abstract Objective Littoral zone aquatic habitat is an important component of sport fish population dynamics in freshwater lakes and reservoirs and is a primary target of fisheries management actions. However, habitat data for these systems are often minimal or nonexistent due to the cost and time-consuming nature of traditional aquatic habitat sampling methods. Side-scan sonar has been identified as a potential tool that can address these limitations, allowing quantification of habitat features over large areas. Side-scan sonar is available in two forms: recreational (consumer grade) and professional (survey grade). Our goal was to compare these two grades of side-scan sonar by analyzing their ability to map littoral habitat features in three Ohio reservoirs. Methods We used a Lowrance Active Imaging 3-in-1 system (≈US$2000) recreational sonar and an EdgeTech 6205 system (≈$150,000) survey-grade sonar to collect imagery in the littoral zones of reservoirs. We manually quantified submerged woody debris, standing timber, aquatic vegetation, and benthic substrate in a geographical information system (GIS) using imagery from each sonar system and compared habitat estimates and GIS processing times. We analyzed how differences in image resolution between the two sonar systems affected the level of variation in habitat classification values generated by individual analysts. Result We found small differences in habitat classification values and accuracy between the two sonar systems, and trade-offs existed in spatial accuracy and ability to image dense vegetation. However, side-scan data acquisition, postprocessing, and habitat classification were generally less time-intensive with the recreational Lowrance system than with the survey-grade EdgeTech system. Unexpectedly, the lower quality Lowrance imagery had less user-based variation in GIS habitat classification. Conclusion Recreational side-scan sonar systems such as the Lowrance system provide sufficient imagery resolution, habitat classification values, and accuracy at a lower cost and with less processing time than survey-grade side-scan sonar systems and are useful tools for quantifying littoral habitat features in reservoirs.

Signal analysis for drone detection and characterization using acoustic radar

Abstract In response to the growing use of drones, this paper focuses on developing a radar-based drone detection system, emphasizing the challenges and opportunities of the Acoustic Radar method. The research addresses the critical challenge of accurately detecting drones to protect sensitive areas and facilities. By tackling the limitations of current drone detection methods, such as optical surveillance, and addressing challenges related to limited datasets, this research innovates analysis for accurate and reliable drone detection and characterization. The methodology involves data acquisition, signal processing using MATLAB, and signal analysis. The system configuration includes wired microphones attached to a tripod and connected to a laptop for data processing and display, set up in an outdoor environment to minimize interference. Quantitative insights are derived through mean and standard deviation calculations, guided by predefined targets reflecting amplitude variations based on drone size and distance. To address the challenge of surrounding sound noise, the system implements digital filters to enhance detection accuracy. The results underscore the effectiveness of the proposed Acoustic Radar method in advancing algorithms for accurate and reliable drone detection and characterization.

A passive sonar based underwater acoustic channel model for improved search and rescue operations in deep sea

A passive sonar based underwater acoustic channel model for improved search and rescue operations in deep sea

Fast Parameter Estimation of Linear Frequency Modulation Signals in Marine Environments Based on Gradient Optimization Strategy

Multi-buoy sonar systems achieve target localization by receiving broadband Linear Frequency Modulation signals emitted from the transmitter. Accurate estimations of the parameters of Linear Frequency Modulation signals significantly enhance the localization accuracy. Linear Frequency Modulation signals can be focused into the fractional domain through Fractional Fourier Transform, but this increases the computational complexity. In marine environments, the low signal-to-noise ratio and multipath effects degrade the parameter estimation accuracy further. To address these issues, this paper proposes a fast estimation algorithm based on the Fractional Fourier Transform and a Gradient Subtraction-Average-Based Optimizer. This algorithm leverages the impulsive characteristics of Linear Frequency Modulation signals after Fractional Fourier Transform transformation, using the Fractional Fourier Transform as the fitness function. The Gradient Subtraction-Average-Based Optimizer algorithm includes three enhancement strategies: chaotic mapping initialization, a Golden Sine Algorithm, and an adaptive t-distribution variational operator. The simulation results demonstrate that the Gradient Subtraction-Average-Based Optimizer algorithm improves the issues of low diversity in the search agents, imbalanced global and local search capabilities, and susceptibility to local optima. A comparative analysis and statistical testing confirm that under a low signal-to-noise ratio and multipath effect conditions, the Gradient Subtraction-Average-Based Optimizer algorithm not only ensures real-time parameter estimation but also improves the estimation accuracy. The results of the parameter estimation provide reliable data support for subsequent target localization.

Underwater Image Recognition using Machine Learning

Machine Learning is the branch of Artificial Intelligence in which a computer is fed with data and based on that data it tries to find out solution on its own. It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase the performance of the algorithms. A Convolutional Neural Network (CNN) is a type of a deep learned an algorithm that has been created for image processing when using convolutional layers to automatically and in a hierarchical way learn features from the input images. Computers can perform well when it comes to image recognition and classification because of its capacity to detect and record such features as edges, or texture, and shapes among others. A rise in focusing on processing underwater images is essential for various research purposes necessary in marine biology, economy as well as in the management of species’ biodiversity. Observance of such organisms as plankton and Posidonia Oceanic allows determining environmental shifts, global warming, and impact of people on sea creatures. These include respectively planktons that are fundamental for oxygen generation, climatic events and the Posidonia Oceanic, which helps improve the sea Biodiversity and water quality. In the organisation study, image processing supplement the physio-chemical analysis and the sonar detection system. The performances of deep learning models, especially the CNNs, in underwater image processing are significantly better than the conventional methodologies. Preprocessing is important because images are often low-quality; data augmentation and transfer learning tackle the problems of a small dataset and class imbalance, which allow you to save computations during training. Through human activities, marine trash remains a menace to deep sea ecosystems and marine organisms calling for proper debris control.

Recognition of Underwater Acoustic Radar Signals Based on Multiresolution and Dense Convolutional Neural Network

Recognition of Underwater Acoustic Radar Signals Based on Multiresolution and Dense Convolutional Neural Network

RANCANG BANGUN ALAT PELATIHAN PARKIR MOBIL BERBASIS MIKROKONTROLER

The increasingly limited parking area certainly requires someone to be more proficient in parking a car. Arduino is an electronic board device equipped with open source software, where this device is included in the ATMega microcontroller family and functions as a single-board microcontroller designed to facilitate the use of electronics in various fields released by Atmel. The ultrasonic sensor HC-SR04 is an ultrasonic sensor that works like a sonar system on Bats and Dolphins where in this sensor there is a sender and receiver, this sensor has a range specification of 2 cm - 400 cm with a resolution of 0.3 cm, and an angle range of less than 15 degrees. To prevent failure or mistakes when parking a car usually takes longer to learn than learning to drive a car itself. LCD (Liquid Crystal Display) functions as a display medium that uses liquid crystals as a display of data, characters, letters and graphics.

Enhancing underwater target detection: Fusion of spatio‐temporal incompletely‐aligned AIS and sonar information via DTW and multi‐head attention mechanism

AbstractIn the field of underwater target detection, the passive sonar is an important means of long‐distance target detection. The sonar detection information typically includes both surface and underwater targets, whereas it is a great challenge on effectively distinguishing between surface and underwater targets solely based on sonar information. Effective fusion of sonar and AIS (Automatic Identification System) data can leverage their complementary nature to compensate for the limitation of sonar information. However, the sonar information and AIS information are acquired based on different detection principles and systems, which are essentially multi‐source heterogeneous information with obvious spatio‐temporal misalignment in nature. Existing fusion methods normally struggle to effectively align sonar and AIS data in both time and space subject to the complexity of the problem. In this study, the Dynamic Time Warping (DTW) algorithm is applied to align sonar and AIS data in the time domain. In addition, a deep learning algorithm with multi‐head attention mechanism is proposed to achieve the spatial alignment of sonar and AIS data, where the matching between the surface targets in AIS data and the same surface targets in sonar data can also be successfully achieved. It provides a priori knowledge to enhance the underwater target detection of the passive sonar by eliminating the interference of the surface targets. Based on the attention mechanism, the abstract features extracted from the intermediate‐layer of the neural networks are found to be effective to represent the typical features of the target motion trajectories, which also demonstrates the effectiveness of the attention mechanism. The experiment results show that the proposed method can successfully achieve a MatchingSucccessRate of over 95% between the AIS targets and sonar detection targets.

Adaptive Line Enhancer for Passive Sonars Based on Frequency-Domain Sparsity, Shannon Entropy Criterion and Mixed-Weighted Error

AbstractAdaptive line enhancer (ALE) is one of the vital signal processing techniques to the detection and recognition of underwater acoustic targets for passive sonars. Conventional ALEs, based on Gaussian noise assumption and least mean square (LMS) algorithm, can achieve good line enhancement property in Gaussian noise background. However, limited by the high steady-state misadjustment of LMS algorithm, the performance of conventional ALEs deteriorates under non-Gaussian noise background and degrades severely in processing signals with comparably lower signal-to-noise ratio (SNR). Therefore, it’s of great necessity to improve the line enhancement performances of ALE techniques to meet the demands of engineering application in passive sonars. In order to optimize the robustness and adaptability of conventional ALEs in dealing with underwater acoustic signals with much lower-SNR and in non-Gaussian noise background, a modified ALE algorithm called frequency-domain ALE based on l1-norm, Shannon entropy criterion and mixed-weighted norm (l1-SE-MWE-FALE) is proposed in this paper. The proposed l1-SE-MWE-FALE algorithm is based on the integration of frequency-domain sparsity, Shannon entropy (SE) criterion along with mixed-weighted error of LMS and least absolute deviation (LAD) to improve the ALE performance in situations above. The simulation results demonstrate that, when the input SNR is as low as – 25 dB, the local SNR (LSNR) gain for line spectrums by l1-SE-MWE-FALE is 9.8 dB, 3.7 dB and 2.3 dB higher than conventional ALE, l1-norm-based frequency-domain ALE (l1-FALE) and l1 norm-Shannon entropy criterion-based frequency-domain ALE (l1-SE-FALE), respectively. Meanwhile, the simulation results also indicate that the parameters of the proposed method can be chosen loosely and hence are insensitive to the choice of their values. Furthermore, the processing results of two different kinds of real ship-radiated noise signals recorded by passive sonars also imply the advantages of the proposed method over the other three ALEs both qualitatively and quantitatively in the respect of line spectrum LSNR gain and parameter insensitivity. The simulation and experiment results both validate the performance insensitivity to parameter adjustment and hence exhibit a good perspective of applications for passive sonars.

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

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

Adaptive stochastic resonance enhanced weak linear frequency modulated signal perception in low signal-to-noise ratio environments

Abstract The linear frequency modulated (LFM) signal has gained extensive utilization in radar, sonar and covert communication system due to its large time-bandwidth product, high-precision distance measurement and low detection probability. The perception of weak LMF signals holds significant importance yet encounters substantial challenges within the increasing complexity of the electromagnetic environments. Consequently, this paper proposes an adaptive stochastic resonance (ASR) enhanced approach for perceiving weak LFM signals in low signal-to-noise ratio (SNR) conditions. This approach initially commences a pioneering study on the quantitative synergistic resonance mechanism among LFM signals, random noise, and nonlinear stochastic resonance (SR) systems. Subsequently, the ASR system’s implementation becomes straightforward through the adaptive adjustment of SR system parameters according to the LFM signal and noise characteristics. This implementation leverages the inherent property of noise energy transfer to signal energy, facilitating the enhancement of ordered weak signals via random noise. Following the enhancement of the output signal by the ASR system, the Wigner–Ville distribution (WVD) transform’s time-frequency concentration property is employed to extract the time-frequency characteristics. Building on the WVD transform, the Radon transform with linear integral projection is applied to further harness the time-frequency domain energy concentration, thereby achieving the effective perception of weak LFM signals. Finally, the effectiveness of the proposed method in improving the perception performance of weak LFM signal in very low SNR conditions is verified through numerical simulations. It is anticipated that this method will have potential application value in fields such as radar, sonar, and communication under complex electromagnetic environments.

Interoperable and scalable echosounder data processing with Echopype

Abstract Echosounders are high-frequency sonar systems used to sense fish and zooplankton underwater. Their deployment on a variety of ocean observing platforms is generating vast amounts of data at an unprecedented speed from the oceans. Efficient and integrative analysis of these data, whether across different echosounder instruments or in combination with other oceanographic datasets, is crucial for understanding marine ecosystem response to the rapidly changing climate. Here we present Echopype, an open-source Python software library designed to address this need. By standardizing data as labeled, multi-dimensional arrays encoded in the widely embraced netCDF data model following a community convention, Echopype enhances the interoperability of echosounder data, making it easier to explore and use. By leveraging scientific Python libraries optimized for distributed computing, Echopype achieves computational scalability, enabling efficient processing in both local and cloud computing environments. Echopype’s modularized package structure further provides a unified framework for expanding support for additional instrument raw data formats and incorporating new analysis functionalities. We plan to continue developing Echopype by supporting and collaborating with the echosounder user community, and envision that the growth of this package will catalyze the integration of echosounder data into broader regional and global ocean observation strategies.

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.

Examination of representative manifolds existence in passive sonar dataset

Conventionally, passive sonar classification has been conducted to predict a ship size for given sample. Recent studies have shown high accuracy, far exceeding the performance of early baselines. However, the assumption that manifolds of passive sonar dataset are formed according to the ship size hasn't been studied deeply. In this paper, we investigate the assumption and attempt to analyze the dataset to observe the manifolds. We consider various attributes of passive sonar signals including, which could form the manifold. We employ unsupervised and supervised learning methods with various extracted features. In our analysis, it is difficult to find an attributes dominantly forming the manifolds because passive signals significantly changed by various facts including ocean environment and moving status. Our study wil provide a deeper understanding of the dataset, leading to a realistic and robust passive sonar classifier.

Uncertainty-aware classification of underwater acoustic signals using Bayesian deep learning

This talk presents our work on applying Bayesian deep learning methods to provide interpretable and uncertainty-aware classification of underwater acoustic signals. We demonstrate how Bayesian convolutional neural networks can accurately classify both passive sonar and synthetic aperture sonar data while quantifying prediction uncertainty—a critical capability for autonomous systems. By examining estimated aleatoric and epistemic uncertainties, we gain insight into the model's decision-making process and areas of low confidence. We leverage predictive entropy and calibrated uncertainties to identify challenging samples, explain misclassifications, and reveal patterns in model behavior. Our results show how uncertainty correlates with factors like range, aspect angle, and seafloor texture in ways that align with acoustic propagation physics. We also discuss how active learning strategies guided by epistemic uncertainty can efficiently train models while providing further model interpretability. This work establishes Bayesian deep learning as a promising approach for developing explainable and reliable underwater acoustic classifiers, addressing the need for transparency in AI-driven underwater sensing applications.

Classification of morphologically separated synthetic aperture sonar imagery via dual-representation convolutional neural networks

Morphological component analysis (MCA) involves the separation of time series into components based on morphological factors, such as duration or envelope type. Acoustic returns from sonar systems can be separated into short-duration and long-duration components using MCA, and the resulting time series formed into traditional synthetic aperture sonar (SAS) images. We present an application of this approach to classification of data from hollow and solid spherical targets collected using a linear in-air SAS system (AirSAS). AirSAS time series were separated into morphological components and, utilizing online image formation, the resulting imagery processed through a multi-branch convolutional neural network (CNN) architecture to classify targets by type. Classification results are included for a variety of scene backgrounds and representation modalities. This is a challenging classification problem since the targets have the same exterior geometry and are distinguished by non-specular acoustic phenomenology.