Underwater Acoustic Detection Research Articles

Research on underwater acoustic detection technology based on optical waveguide resonator cavity

Purpose In acoustic detection technology, optical microcavities offer higher detection bandwidth and sensitivity than traditional acoustic sensors. However, research on acoustic detection technologies involving optical microcavities has not yet been reported. Therefore, this paper aims to design and construct an underwater acoustic detection system based on optical microcavities and study its acoustic detection technology to improve its performance. Design/methodology/approach Based on the principles of optical microcavity acoustic sensors, a signal-detection circuit was designed to form a detection system in conjunction with a laser, an optical waveguide resonator and an oscilloscope. This circuit consists of two modules: a photodetection module and a filter amplification module. Findings The photodetection module features a baseline noise of −106.499 dBm and can detect device spectral line depths of up to 2410 mV. The gain stability of the filter amplification module was 58 dB ± 1 dB with a noise gain of −107.626 dBm. This design allows the acoustic detection system to detect signals with high sensitivity within the 10 Hz−1.2 MHz frequency band, achieving a maximum sensitivity of −126 dB re 1 V/µPa at 800 Hz and a minimum detectable pressure (MDP) of 0.37 mPa/Hz1/2, corresponding to a noise equivalent pressure (NEP) of 51.36 dB re 1 V/µPa. Originality/value This study designs and constructs a broadband underwater acoustic detection system specifically for optical waveguide resonators based on the sensing principles of silicon dioxide optical waveguide resonators. Experiments demonstrated that the signal detection module improves the sensitivity of underwater acoustic detection based on optical waveguides.

Theoretical and experimental study of a two-dimensional circular stealth acoustic lens

The detection of underwater acoustic signals is a key focus in ocean development, and acoustic metamaterials play a crucial role in achieving this goal. Pentamode metamaterials exhibit fluid-like properties, allowing for the propagation of compressional waves without shear waves. In this study, a two-dimensional circular stealth acoustic lens based on pentamode metamaterials was proposed. By applying the transformation acoustics theory, the theoretical properties of circular stealth acoustic lenses were calculated, and they were verified with Finite Element Method (FEM). A practical model was designed with pentamode metamaterials and silicone oil, and its performance was verified with FEM software. A half-model was fabricated, and underwater experiments demonstrate that it does not effectively affect the propagation state of acoustic waves. The consistency between theoretical results and experimental findings highlights practical significance and broad application prospects for the circular stealth acoustic lens in the field of acoustic detection.

DA-YOLOv7: A Deep Learning-Driven High-Performance Underwater Sonar Image Target Recognition Model

Affected by the complex underwater environment and the limitations of low-resolution sonar image data and small sample sizes, traditional image recognition algorithms have difficulties achieving accurate sonar image recognition. The research builds on YOLOv7 and devises an innovative fast recognition model designed explicitly for sonar images, namely the Dual Attention Mechanism YOLOv7 model (DA-YOLOv7), to tackle such challenges. New modules such as the Omni-Directional Convolution Channel Prior Convolutional Attention Efficient Layer Aggregation Network (OA-ELAN), Spatial Pyramid Pooling Channel Shuffling and Pixel-level Convolution Bilat-eral-branch Transformer (SPPCSPCBiFormer), and Ghost-Shuffle Convolution Enhanced Layer Aggregation Network-High performance (G-ELAN-H) are central to its design, which reduce the computational burden and enhance the accuracy in detecting small targets and capturing local features and crucial information. The study adopts transfer learning to deal with the lack of sonar image samples. By pre-training the large-scale Underwater Acoustic Target Detection Dataset (UATD dataset), DA-YOLOV7 obtains initial weights, fine-tuned on the smaller Smaller Common Sonar Target Detection Dataset (SCTD dataset), thereby reducing the risk of overfitting which is commonly encountered in small datasets. The experimental results on the UATD, the Underwater Optical Target Detection Intelligent Algorithm Competition 2021 Dataset (URPC), and SCTD datasets show that DA-YOLOV7 exhibits outstanding performance, with mAP@0.5 scores reaching 89.4%, 89.9%, and 99.15%, respectively. In addition, the model maintains real-time speed while having superior accuracy and recall rates compared to existing mainstream target recognition models. These findings establish the superiority of DA-YOLOV7 in sonar image analysis tasks.

A Dual-Stream Deep Learning-Based Acoustic Denoising Model to Enhance Underwater Information Perception

Estimating the line spectra of ship-radiated noise is a crucial remote sensing technique for detecting and recognizing underwater acoustic targets. Improving the signal-to-noise ratio (SNR) makes the low-frequency components of the target signal more prominent. This enhancement aids in the detection of underwater acoustic signals using sonar. Based on the characteristics of low-frequency narrow-band line spectra signals in underwater target radiated noise, we propose a dual-stream deep learning network with frequency characteristics transformation (DS_FCTNet) for line spectra estimation. The dual streams predict amplitude and phase masks separately and use an information exchange module to swap learn features between the amplitude and phase spectra, aiding in better phase information reconstruction and signal denoising. Additionally, a frequency characteristics transformation module is employed to extract convolutional features between channels, obtaining global correlations of the amplitude spectrum and enhancing the ability to learn target signal features. Through experimental analysis on ShipsEar, a dataset of underwater acoustic signals by hydrophones deployed in shallow water, the effectiveness and rationality of different modules within DS_FCTNet are verified.Under low SNR conditions and with unknown ship types, the proposed DS_FCTNet model exhibits the best line spectrum enhancement compared to methods such as SEGAN and DPT_FSNet. Specifically, SDR and SSNR are improved by 14.77 dB and 13.58 dB, respectively, enabling the detection of weaker target signals and laying the foundation for target localization and recognition applications.

Acoustic wave propagation in depth-evolving sound-speed field using the lattice Boltzmann method

This study investigates the propagation of sound waves within deep-sea low-sound-speed channels using the lattice Boltzmann method, with a key focus on the influence of depth-dependent sound speed on wave propagation. The depth-variable sound speed condition is realized through the incorporation of an external force proportional to the density gradient. After the model verification, investigations into the two-dimensional spreading of sound sources reveal that the depth-dependent sound speed curves the wave propagation. When source depths differing from the low-sound-speed channel, wave paths deviate due to contrasting speeds above and below. When the sound source is situated within the low-sound-speed channel, waves exhibit converging patterns. The simulations also detail the total reflection behavior of sound waves. When the incident angle falls exceeds the critical angle, the waves remain intact within the low-sound-speed channel, thereby enabling the preservation of high amplitude acoustic signals even at remote locations. The subsequent simulations of sound wave propagation around obstacles demonstrate that the low-sound-speed channel also exhibits better signal transmission capabilities in the presence of obstacles. In a uniform sound speed environment, acoustic wave propagation around a submarine exhibits a symmetric pattern. By contrast, under depth-evolving speed conditions, submarines operating at various depths manifest distinct propagation characteristics, such as asymmetric wave propagation during shallow diving, as well as wave attenuation or even silencing when cruising within low-sound-speed channels. These findings underscore the profound implications of depth-evolving sound speed on underwater acoustic signal detection and transmission.

Detection range of a passing submerged emitter of sound in ambient noisea).

An underwater acoustic detection problem is studied in which the ambient noise present at a receiver is calculated, given information describing environmental conditions, including windspeed, and the positions of nearby ships which act as sources of background noise. The signal, whose detection is sought, is narrowband and transmitted from a source that passes by the receiver along a straight track. Cumulative Probability of Detection (CPoD) is calculated along a series of tracks with increasing closest-point-of-approach distances to the receiver. Two detection ranges are analyzed, a so-called "defender" detection range and an "intruder" detection range. Both are conservative measures associated with CPoD equaling 0.5 during the transit of the submerged vessel. Predictions of the detection range are compared across independent attempts to solve the same problem with different modelling approaches. The spread of results (i.e., the "reproducibility" of the predictions) is discussed and reasons for differences are highlighted. Environmental conditions that strongly affect detection performance are discussed, as is the use of CPoD as a single-valued metric to describe detection performance.

Embedded UUV conformal MEMS vector hydrophone

As the core equipment of Underwater acoustic detection, MEMS vector hydrophones are applied to Unmanned Underwater Vehicles (UUV), which can better detect underwater targets. Most of the MEMS vector hydrophones currently used are long-tube cylindrical, relatively large in volume and weight, which is not suitable for installation on UUV. The co-vibration vector hydrophone is small in size and weight, but it needs elastic suspension due to the requirements of the working principle. Therefore, according to the needs of practical engineering applications, this paper designs an Embedded UUV Conformal MEMS Vector Hydrophone (ECVH), establishes an encapsulation model through theoretical analysis, and uses COMSOL6.0 to simulate its acoustic performance. The sensitivity and directionality of the ECVH are verified in the standing wave barrel. When the test frequency is 630 Hz, the sensitivity of the vector channel is −182.3 dB, and the depth of the ' 8 ' direction concave point is greater than 35 dB. The sensitivity of the sound pressure channel is −182 dB, which is omnidirectional. Finally, it is mounted on UUV for preliminary signal acquisition verification test. The test results show that the ECVH can work normally on UUV, which provides a new idea for underwater small platform test.

Localization and detection of underwater acoustic communication signals using convolutional recursive neural networks

Abstract This paper employs a multi-task neural network (UWAELD) to simultaneously perform the Underwater Acoustic Communication Event Detection (UACED) and direction-of-arrival (DOA) mission. Typically, the UACED and DOA tasks are executed separately, as the UACED task is identified through time-frequency analysis, while the DOA task is identified through the amplitude and phase differences of the signal, making it very difficult to jointly perform both tasks. To overcome this, we utilize a logarithmic spectrogram for time-frequency analysis and stack the normalized eigenvectors obtained by processing the signal covariance matrix. We ultimately evaluate the network’s feasibility using simulation data and lake test data, utilizing acoustical signals measured in actual experiments. Finally, for the UADCED task, the error rate is only 6%, with an F1 score of 0.83; for the DOA task, the localization error is only 1.72°, with a localization recall score of 0.87. Additionally, for the DOA task, we employ the classical MUSIC algorithm to demonstrate the network’s superiority.

A deep learning method for reflective boundary estimation

Environment estimation is a challenging task in reverberant settings such as the underwater and indoor acoustic domains. The locations of reflective boundaries, for example, can be estimated using acoustic echoes and leveraged for subsequent, more accurate localization and mapping. Current boundary estimation methods are constrained to high signal-to-noise ratios or are customized to specific environments. Existing methods also often require a correct assignment of echoes to boundaries, which is difficult if spurious echoes are detected. To evade these limitations, a convolutional neural network (NN) method is developed for robust two-dimensional boundary estimation, given known emitter and receiver locations. A Hough transform-inspired algorithm is leveraged to transform echo times of arrival into images, which are amenable to multi-resolution regression by NNs. The same architecture is trained on transform images of different resolutions to obtain diverse NNs, deployed sequentially for increasingly refined boundary estimation. A correct echo labeling solution is not required, and the method is robust to reverberation. The proposed method is tested in simulation and for real data from a water tank, where it outperforms state-of-the-art alternatives. These results are encouraging for the future development of data-driven three-dimensional environment estimation with high practical value in underwater acoustic detection and tracking.

Research on reconstruction of the global sound speed profile combining partial underwater prior information

The sound speed profile (SSP) is an important factor affecting the acoustic propagation characteristics of the ocean, making the accurate acquisition of SSP a crucial step in the interdisciplinary research of oceanography and underwater acoustics. Limited by the cost of in-situ measurement and the performance of the instrument itself, direct measurement of SSP inevitably leads to insufficient depth or even missing information. In this paper, we propose using partial underwater prior information (UWPI) only including underwater sound speed to obtain preliminary reconstruction results of global SSP for the first time. The empirical orthogonal function (EOF) reconstruction algorithm is optimized by employing assimilated SSP as the background SSP to further reduce reconstruction errors. The maximum global average reconstruction error and root mean square error (RMSE) after optimization decrease by >51% and 71%, respectively, which indicates that the performance of the optimized algorithm combined with partial UWPI is further improved. Finally, the performance of the optimized algorithm is discussed from the perspective of acoustic propagation. This research provides a reliable technical approach for SSP reconstruction under incomplete depth conditions, which can be applied in underwater sound field prediction and acoustic detection in the future.

Underwater Non-stationary Acoustic Signal Detection Based on the STHOC Noise Suppression

Underwater Non-stationary Acoustic Signal Detection Based on the STHOC Noise Suppression

Miniaturized and highly sensitive fiber-optic Fabry–Perot sensor for mHz infrasound detection

Infrasound detection is important in natural disasters monitoring, military defense, underwater acoustic detection, and other domains. Fiber-optic Fabry–Perot (FP) acoustic sensors have the advantages of small structure size, long-distance detection, immunity to electromagnetic interference, and so on. The size of an FP sensor depends on the transducer diaphragm size and the back cavity volume. However, a small transducer diaphragm size means a low sensitivity. Moreover, a small back cavity volume will increase the low cut-off frequency of the sensor. Hence, it is difficult for fiber-optic FP infrasound sensors to simultaneously achieve miniaturization, high sensitivity, and extremely low detectable frequency. In this work, we proposed and demonstrated a miniaturized and highly sensitive fiber-optic FP sensor for mHz infrasound detection by exploiting a Cr-Ag-Au composite acoustic-optic transducer diaphragm and a MEMS technique-based spiral micro-flow hole. The use of the spiral micro-flow hole as the connecting hole greatly reduced the volume of the sensor and decreased the low-frequency limit, while the back cavity volume was not increased. Combined with the Cr-Ag-Au composite diaphragm, a detection sensitivity of −123.19 dB re 1 rad/μPa at 5 Hz and a minimum detectable pressure (MDP) of 1.2 mPa/Hz1/2 at 5 Hz were achieved. The low detectable frequency can reach 0.01 Hz and the flat response range was 0.01–2500 Hz with a sensitivity fluctuation of ±1.5 dB. Moreover, the size of the designed sensor was only 12 mm×Φ12.7 mm. These excellent characteristics make the sensor have great practical application prospects.

New Perceptions of Ancient Commerce Driven by Underwater Ancient Site Investigations: A Case Study of Xinfeng River Basin

In the 1950s and 1960s, to address the flooding issues and power shortage that hindered national construction, the Xinfeng River hydropower plant was planned and built to prevent floods, store water, and generate electricity. Consequently, many ancient ruins in the study area were drowned, including ancient post roads, channels, villages, towns, bridges, and other relic sites. By checking historical data and adopting integrated underwater acoustic detection, we conducted a comprehensive cultural-relics survey on the flooded area under Wanlv Lake in the Xinfeng River Basin. A side-scan sonar detection of the underwater relics within the flooded area confirmed the spatial distribution of cultural relics in the Xinfeng River Basin. It portrayed ancient people’s production and life scenarios, outlined the migration and trade history within the region and beyond, and contributed to the enrichment of the literature and understanding of ancient shipping and trade in the basin.

An integrated waveform design method for underwater acoustic detection and communication

AbstractIn recent years, the integrated system for underwater detection and communication (ISUDC) has become one of the important research directions. At present, the efficiency of the existing shared signals carrying communication information is low, and the synchronization performance needs to be further improved. So, this paper proposes an integrated waveform design scheme for ISUDC based on the carrier with good detection performance and binary phase shift differential keying (DBPSK). In this scheme, the signal with better underwater detection performance (e.g. Linear Frequency Modulation, LFM) is selected as the carrier, and DBPSK is used as the modulation mode to modulate the communication symbols into the carrier. However, the correlation of the detection signal will be destroyed. Thus, the down‐frequency detection signal (e.g. ngLFM) is superimposed into the signal to form a shared signal (LFM‐DBPSK‐ng, LDN). The detection performance, synchronization performance and communication performance of the shared signal are analyzed by ambiguity function, cross‐correlation and BER, respectively. In addition, according to the proposed scheme, the shared signal HFM‐DBPSK‐ng (HDN) is generated by using HFM as carrier. The comparative simulation experiments are carried out with the existing LFM‐BPSK signals. Experiments show that both LDN and HDN have better performance compared to the other shared signals, which proves the effectiveness of the integrated waveform design scheme for ISUDC proposed in this paper.

Seafloor Sediment Acoustic Properties on the Continental Slope in the Northwestern South China Sea

The acoustic properties of seafloor sediments on continental slopes play a crucial role in underwater acoustic propagation, communication, and detection. To investigate the acoustic characteristics and spatial distribution patterns of sediments on the continental slope, a geoacoustic experiment was conducted in the northwestern South China Sea. The experiment covered two sections: one crossing the shelf and slope in the downslope direction, and the other near the shelf break in the along-slope direction. In situ techniques, sediment sampling, and laboratory measurements were used to acquire data on sediment acoustic properties (such as sound speed and attenuation) and physical properties (including particle composition, density, porosity, and mean grain size). The experimental findings revealed several key points: (1) Acoustic properties of shallow water coarse-grained sediments and deep-sea sediments were higher when measured in the laboratory compared to in situ measurements. (2) Relationships between measured attenuation and physical properties, as well as between sound speed and mean grain size, showed deviations from previous empirical equations. (3) Sediment acoustic and physical properties exhibited significant variations in the downslope direction, while showing gradual variations in the along-slope direction. These variations can be attributed to sedimentary environmental factors such as material sources, hydrodynamic conditions, and water depth.

Underwater Rescue Target Detection Based on Acoustic Images.

In order to effectively respond to floods and water emergencies that result in the drowning of missing persons, timely and effective search and rescue is a very critical step in underwater rescue. Due to the complex underwater environment and low visibility, unmanned underwater vehicles (UUVs) with sonar are more efficient than traditional manual search and rescue methods to conduct active searches using deep learning algorithms. In this paper, we constructed a sound-based rescue target dataset that encompasses both the source and target domains using deep transfer learning techniques. For the underwater acoustic rescue target detection of small targets, which lack image feature accuracy, this paper proposes a two-branch convolution module and improves the YOLOv5s algorithm model to design an acoustic rescue small target detection algorithm model. For an underwater rescue target dataset based on acoustic images with a small sample acoustic dataset, a direct fine-tuning using optical image pre-training lacks cross-domain adaptability due to the different statistical properties of optical and acoustic images. This paper therefore proposes a heterogeneous information hierarchical migration learning method. For the false detection of acoustic rescue targets in a complex underwater background, the network layer is frozen during the hierarchical migration of heterogeneous information to improve the detection accuracy. In addition, in order to be more applicable to the embedded devices carried by underwater UAVs, an underwater acoustic rescue target detection algorithm based on ShuffleNetv2 is proposed to improve the two-branch convolutional module and the backbone network of YOLOv5s algorithm, and to create a lightweight model based on hierarchical migration of heterogeneous information. Through extensive comparative experiments conducted on various acoustic images, we have thoroughly validated the feasibility and effectiveness of our method. Our approach has demonstrated state-of-the-art performance in underwater search and rescue target detection tasks.

Machine learning–based feature prediction of convergence zones in ocean front environments

The convergence zone holds significant importance in deep-sea underwater acoustic propagation, playing a pivotal role in remote underwater acoustic detection and communication. Despite the adaptability and predictive power of machine learning, its practical application in predicting the convergence zone remains largely unexplored. This study aimed to address this gap by developing a high-resolution ocean front-based model for convergence zone prediction. Out of 24 machine learning algorithms tested through K-fold cross-validation, the multilayer perceptron–random forest hybrid demonstrated the highest accuracy, showing its superiority in predicting the convergence zone within a complex ocean front environment. The research findings emphasized the substantial impact of ocean fronts on the convergence zone’s location concerning the sound source. Specifically, they highlighted that in relatively cold (or warm) water, the intensity of the ocean front significantly influences the proximity (or distance) of the convergence zone to the sound source. Furthermore, among the input features, the turning depth emerged as a crucial determinant, contributing more than 25% to the model’s effectiveness in predicting the convergence zone’s distance. The model achieved an accuracy of 82.43% in predicting the convergence zone’s distance with an error of less than 1 km. Additionally, it attained a 77.1% accuracy in predicting the convergence zone’s width within a similar error range. Notably, this prediction model exhibits strong performance and generalizability, capable of discerning evolving trends in new datasets when cross-validated using in situ observation data and information from diverse sea areas.

A new underwater acoustic signal denoising method based on modified uniform phase empirical mode decomposition, hierarchical amplitude-aware permutation entropy, and optimized improved wavelet threshold denoising

Underwater acoustic signal (UAS) denoising is base and prerequisite for UAS detection, recognition and classification. In order to perform UAS denoising effectively, a new UAS denoising method based on modified uniform phase empirical mode decomposition (MUPEMD), hierarchical amplitude-aware permutation entropy (HAAPE), and improved wavelet threshold denoising (IWTD) method optimized by sand cat swarm optimization (SCSO) (SIWTD), named MUPEMD-HAAPE-SIWTD, is proposed. Firstly, decompose original signal into a battery of intrinsic mode functions (IMFs) by MUPEMD. Secondly, determine the double threshold by HAAPE to classify signal into pure signal, mixed signal and noisy signal. Then, optimize IWTD by SCSO, so that it can adaptively select the optimal wavelet basis function to achieve the denoising of mixed signal. Finally, the denoised mixed signal is reconstructed with pure signal to obtain ultima denoised signal. The denoising experiments on Lorenz signal and Duffing signal demonstrate that signal-to-noise ratio can be improved by 9 dB–13 dB by the proposed method. The denoising experiments on simulating ship radiated noise signals and four kinds of actual ship radiated noise signals (https://www.aigei.com/, accessed on June 1, 2023) demonstrate that the proposed method makes phase diagram smoother and clearer, and makes the ability of suppressing noise higher.

Semi-analytical solution for sound propagation from a moving directional source in a shallow-water waveguide

Understanding the waveguide fields produced by moving directional sources is of crucial importance in underwater acoustic detection. In practice, moving underwater targets often exhibit directionality, especially at high frequencies. Herein, we present a semi-analytical method to calculate the Doppler-shifted field (DSF) from a directional source moving horizontally in a range-independent, shallow-water waveguide. We develop a novel normal-mode model using a modal-projection approach with a perfectly matched layer. This comprehensively accounts for changes in modal shape, eigenvalue shifts, and the branch-integral contribution. We derive an analytical expression for the DSF based on Huygens’ principle, and the solution is presented as a sum over propagating eigenmodes, with the modal excitation determined by the source directivity depending upon the grazing angle of each pair of modal plane waves. This expression is valid for any type of radiator moving in a range-independent environment with a smoothly varying sound-speed profile. We then present a theoretical analysis of the Doppler beam shift produced by a piston-like radiator. This reveals that the observed angular offset of modal plane waves can be attributed to the Doppler beam shift and confirms that this is a new mechanism causing considerable differences between the fields generated by a directional moving source and by the same source at rest. This has potential value in various applications, particularly for underwater acoustic detection of moving targets.

MTSA-Net: A multiscale time self-attention network for ship radiated self-noise reduction

For a surface ship carrying a towed array, its in-band radiated self-noise is one of the near-field strong interference, which will seriously limit the performance of the underwater acoustic (UWA) signal detection system. Typically, the conventional ship noise cancellation methods requires time-synchronized hydrophone arrays, such as the towed linear array (TLA), to suppress its self-noise by using spatial filters. However, the spatial filter based methods fail when the direction of arrival of the desired signal is in the ship noise masking area. In cooperative scenarios, the prior knowledge of the template signal provides additional temporal information, which can be utilized to design a time-domain representations based detection system. In this paper, a multiscale time self-attention network (MTSA-Net) is proposed to mitigate the ship radiated self-noise and enhance the desired signal to improve the performance of signal detection system. Experimental results based on sea trial data indicate the effectiveness of our proposed method.