Underwater Acoustic Detection Research Articles

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.

Fourier transformation for acoustic: Principle & applications

A The Fourier transform has a wide range of applications in daily life, including physics, signal processing, acoustics etc. The topic of this article is to demonstrate the principle and applications of the Fourier transform in acoustics through theoretical derivation. The paper first derived the basic formula of the Fourier Transform and the related seven theorems. Then the paper detailed the research of Fourier Transform in underwater acoustic pulse signal detection technology. Finally, the application of Fourier Transform in the defect detection algorithm of MEMS acoustic films was detailed. According to the analysis, the paper demonstrated the primary application of Fourier Transform in underwater acoustic pulse signal detection and defect detection algorithm of MEMS acoustic film. Based on the evaluations, this study demonstrated the general application scenario of Fourier Transform and offered theoretical basis of its application in acoustic field, which promotes its developmental potential in the acoustic field. Overall, these results shed light on guiding further exploration of acoustic research.

A Robust Denoised Algorithm Based on Hessian–Sparse Deconvolution for Passive Underwater Acoustic Detection

Digital beamforming techniques find wide applications in the field of underwater acoustic array signal processing. However, their azimuthal resolution has long been constrained by the Rayleigh limit, consequently limiting their detection performance. In this paper, we propose a novel two-dimensional Hessian–sparse deconvolution algorithm based on image processing techniques. This method assumes a priori that the underwater acoustic bearing time record (BTR) images exhibit sparsity, and then it first constructs partial differential equations in the beamforming domain with sparsity-norm constraints for optimal noise reduction. Subsequently, a two-dimensional deconvolution operation is applied to narrow the main lobe, aiming to achieve additional temporal gains in two-dimensional processing. The simulation and real sea trial data processing results show that the main lobe width of the proposed method is about 1.3 degrees at 0 dB. It effectively reduces the main lobe width and enhances the detection resolution of BTRs in the post-processing part, especially in low-signal-to-noise-ratio (SNR) environments. Therefore, the proposed method provides nice opportunities to further improve the target-detecting ability of hydrophone arrays.

Extraction and analysis of three-dimensional sound scattering characteristics by body-generated internal waves

The motion of an object submerged in a stratified fluid generates surface wakes, and simultaneously induces internal waves at the interface where there is a change in sound speed, known as the thermocline. As a result, spectral-temporal fluctuations occur in both the surface height and the distribution of sound velocity. While surface wakes primarily contribute to geometric acoustic scattering, the internal waves generated by the underwater object's motion can have diverse effects on sound propagation, leading to a prolonged acoustic impact that may have practical applications in underwater acoustic detection. This paper investigates the impact of body-generated internal waves on underwater acoustic propagation through the establishment of an “unfrozen field,” range-dependent model using the approximated Kelvin wake theory. The model allows numerical simulations to demonstrate the spatial-temporal coherence, time-frequency modulation and directional characteristics of the three-dimensional sound field scattered by the body-generated internal wave. By analyzing the influences of thermocline depth, target motion velocity and source depth, the results presented in this study indicate that the long-range acoustic propagation, modulated by the body-generated internal waves, can provide additional information for detecting moving targets.

Long-range underwater acoustic detection of aircraft surface impacts—The influence of acoustic propagation conditions and impact parameters

The 2019 crash of an F35 fighter aircraft off the east coast of Japan was detected at a range of approximately 3000 km by a hydroacoustic station of the Comprehensive Nuclear Test Ban Treaty Organisation’s International Monitoring System. In this paper we compare the acoustic propagation conditions relating to that detection to those relating to the crash of Malaysian Airlines flight MH370 in the Indian Ocean in 2014 with a view to reassessing the likelihood that acoustic signals detected on a similar hydroacoustic station could have been related to the loss of MH370. Possible differences in source levels due to different assumptions about impact parameters are also considered. Despite several extensive searches, the location of the wreckage of MH370 remains unknown.

Theoretical investigation of sound propagation from a moving directional source in a shallow-water waveguide

Doppler phenomena resulting from a moving directional source can be complicated in shallow water environments. This study presents a semi-analytical method to calculate the Doppler-shifted field (DSF) caused by a directional source moving horizontally in a shallow-water waveguide. First, an improved normal-mode (NM) model is developed to comprehensively account for the Doppler-induced changing modal shapes, eigenvalue shifts, and the contribution of the branch cut integral. Next, an analytical, modal expression for the DSF is derived based on the principles of Huygens and Taylor series expansion. The solution is expressed 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 modal expression is applicable to any type of radiators moving in a shallow-water environment with an arbitrary sound speed profile. By employing the proposed theoretical model, we analyze the beam Doppler shift produced by a piston-like radiator moving in a two-layer waveguide. The simulation indicates that an apparent asymmetry arises in the DSF when the beam grazing angle sweeps over the critical angle of the seafloor. This finding confirms that the beam Doppler shift acts as the third mechanism causing significant differences between the field generated by a directional moving source and that by the same source at rest, which may have practical applications in underwater acoustic detection for moving sources.

Effect of electric impedance on the electromechanical characteristics of tunable spherical piezoelectric transducer

Spherical piezoelectric transducers are a critical component of ultrasonic vibration systems with significant applications in various practical scenarios such as underwater acoustic detection and structural health monitoring. The existing spherical piezoelectric transducers have fixed electromechanical characteristics that limit their applications in scenarios with multi-frequency or frequency variation requirements. It would therefore be intriguing, from the viewpoints of both science and technology, to break through this limit by introducing external electric load impedance. Here we propose a tunable spherical piezoelectric transducer (TSPT) capable of adjusting the electromechanical characteristics by changing the resistance, inductance, and capacitance applied to the passive piezoceramic shell. The theoretical and numerical results show that when the resistance is exerted on the inner and outer piezoceramic shells, the resonance is accompanied by large variations at low values of resistance, while it becomes almost constant at high values of resistance. When the inductance is exerted on the outer piezoceramic shell, the resonance frequency obtained by the finite element method can be changed from 72730 Hz to 42928 Hz by adjusting the inductance, indicating that the TSPT can realize a rich and selective resonance frequency region. The resonance/antiresonance frequency decreases while the effective electromechanical coupling coefficient increases, when the capacitance applied on the inner and outer piezoceramic shells increases. The experiments are conducted to verify the effectiveness of the proposed TSPT, which is in agreement with the simulated results and theoretical predictions. Our methodology will offer possibilities to extend the working frequency range of the existing piezoelectric spherical transducer for underwater acoustic detection, hydrophones, and the spherical smart aggregate for civil structural health monitoring.

Systematic design and experimental realization of a radially cascaded spherical piezoelectric transducer.

As a critical component of ultrasonic vibration systems, piezoelectric transducers play an essential role in various practical application scenarios. Recent advances in spherical transducers have been widely used in underwater sound and structural health monitoring, while the cascaded spherical piezoelectric transducer with arbitrary piezoceramic shell thickness has not been investigated. Here, we propose a radially cascaded spherical piezoelectric transducer (RCSPT) and derive its electromechanical equivalent circuit with mechanical losses, dielectric losses, and load mechanical impedances. The resulting device is composed of three concentric spherical metal shells and two radially polarized spherical piezoceramic shells. The underlying physical mechanism is the inverse piezoelectric effect, which converts electrical signals into mechanical vibrations. The effects of the spherical piezoceramic shell's thickness and location on the RCSPT are studied. We also analyze the effects of mechanical losses, dielectric losses, and load mechanical impedances on the modulus of input electric impedance of the cascaded spherical transducer. The experiments are conducted to verify the electromechanical characteristics of the resulting device, which are in good agreement with the simulated results and theoretical predictions. Our methodology will offer new possibilities for designing RCSPTs and may promote applications in various fields, such as underwater acoustic detection and structural health monitoring.

Quantitative Analysis on the Influence of the Oceanic Front on Underwater Acoustic Detection with Investigated Marine Data

At present, some shortcomings of the research on coupling modeling of the oceanic front–sound field may need attention: (1) Most of the acoustic propagation simulation is based on ideal front models, but the application of investigated marine data is lacking. (2) Most studies focus on the acoustic field characteristics, with the influence of fronts on acoustic propagation, but few studies aim at the direct quantitative analysis of the performance of underwater acoustic detection in oceanic fronts. To deal with the above problems, based on the measured data in the northwest Pacific Ocean, here, we first design different sound source layout schemes and calculate sound field characteristics in the sub-Arctic front using the ray theory. Then, the cumulative detection probability model is built based on the active sonar equation to evaluate the efficiency of underwater detection. Finally, the detection probability is calculated and expressed by regionalization, and the influence of the sub-Arctic front on underwater detection is quantitatively analyzed. The results show that the sub-Arctic front can significantly affect the underwater acoustic detection. The sound source located in the front in the cold-water mass has a better detection performance, especially detecting towards cold water (horizontal detection range > 60 km). In contrast, the sound source located in the warm-water mass has a poor detection performance (horizontal detection range in shallow sea < 10 km).

The Effect of Suspended Sediment Concentration on Acoustic Propagation Based on Satellite Data Inversion

Suspended sediment in seawater not only affects the optical properties of seawater, such as transparency and color, but it also greatly affects the process of underwater acoustic propagation. The underwater acoustic detection equipment is affected by the change in suspended sediment concentration (SSC) in use. To address this problem, this paper deeply investigates the correlation between the characteristic parameters of acoustic propagation and SSC in different types of water bodies. It identifies the statistical characteristics of the temporal and spatial distributions of SSC in the Chinese seas through quantitative inversion based on satellite remote sensing data. It further analyzes the relationship between the concentration and distribution of suspended sediment and the characteristics of acoustic propagation of underwater weapons and equipment. Finally, it quantitatively evaluates the effect of SSC on the acoustic attenuation coefficient. This study can provide valuable references for the functional departments and base-level units of naval forces to make decisions and strategies for using underwater acoustic detection equipment.

A deep-learning based high-gain method for underwater acoustic signal detection in intensity fluctuation environments

To achieve efficient underwater acoustic (UWA) signal detection in low signal-to-noise ratio (SNR) scenarios, the transmitted signals are designed with a large time-bandwidth product to get a high detection gain. The linear correlator (LC) is considered as the maximum SNR detector, whose detection gain is proportional to the time-bandwidth product. However, the detection performance of LC degrades significantly in time-variant multipath UWA channel and non-Gaussian UWA ambient noise. In this study, we present a deep-learning based two-stage UWA signal detection method in intensity fluctuation environments. This method takes the advantages of the Conv-TasNet and Encoder-Decoder network, which utilizes an encoder module to extract signal features, a separation module to enhance the signal components and then another decoder module to reconstruct the transmitted signal. To demonstrate the performance of the proposed method, the datasets used for training and testing originated from the ASIAEX 2001 South China Sea (SCS) experiment. The experimental results show that our model outperforms the classical LC and channel estimation based LC (CE-LC) in constant false alarm rate (CFAR) detection and also surpasses the TCDAE and Conv-Tasnet models as evaluated by DSI-SNR1 and DSI-SNR2.