Underwater Acoustic Signals Research Articles

Research on developing an adaptive algorithm for enhancing the quality of underwater positioning based on the sonar USBL principle

The paper proposes an enhanced solution for improving the accuracy of underwater object positioning based on the Ultra-Short Baseline (USBL) sonar principle. In shallow water regions, complex hydrological phenomena affect the quality of underwater acoustic signal transmission and reception, making it essential to enhance the processing of these signal groups. Through the USBL system model, the paper presents a model capable of adaptively selecting accurate signals amidst the noisy underwater environment. The proposed model is implemented and validated in practice using FPGA hardware in Lan Ha Bay. The results demonstrate the effectiveness of the proposed algorithm compared to conventional approaches, as it extends the management range and optimizes the transmission power of the system, even in cases of low signal-to-noise ratio.

Model for Underwater Acoustic Target Recognition with Attention Mechanism Based on Residual Concatenate

Underwater acoustic target recognition remains a formidable challenge in underwater acoustic signal processing. Current target recognition approaches within underwater acoustic frameworks predominantly rely on acoustic image target recognition models. However, this method grapples with two primary setbacks; the pronounced frequency similarity within acoustic images often leads to the loss of critical target data during the feature extraction phase, and the inherent data imbalance within the underwater acoustic target dataset predisposes models to overfitting. In response to these challenges, this research introduces an underwater acoustic target recognition model named Attention Mechanism Residual Concatenate Network (ARescat). This model integrates residual concatenate networks combined with Squeeze-Excitation (SE) attention mechanisms. The entire process culminates with joint supervision employing Focal Loss for precise feature classification. In our study, we conducted recognition experiments using the ShipsEar database and compared the performance of the ARescat model with the classic ResNet18 model under identical feature extraction conditions. The findings reveal that the ARescat model, with a similar quantity of model parameters as ResNet18, achieves a 2.8% higher recognition accuracy, reaching an impressive 95.8%. This enhancement is particularly notable when comparing various models and feature extraction methods, underscoring the ARescat model’s superior proficiency in underwater acoustic target recognition.

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.

Research on the Threshold Determination Method of the Duffing Chaotic System Based on Improved Permutation Entropy and Poincaré Mapping.

The transition from a chaotic to a periodic state in the Duffing chaotic oscillator detection system is crucial in detecting weak signals. However, accurately determining the critical threshold for this transition remains a challenging problem. Traditional methods such as Melnikov theory, the Poincaré section quantitative discrimination method, and experimental analyses based on phase diagram segmentation have limitations in accuracy and efficiency. In addition, they require large computational data and complex algorithms while having slow convergence. Improved permutation entropy incorporates signal amplitude information on the basis of permutation entropy and has better noise resistance. According to the characteristics of improved permutation entropy, a threshold determination method for the Duffing chaotic oscillator detection system based on improved permutation entropy (IPE) and Poincaré mapping (PM) is proposed. This new metric is called Poincaré mapping improved permutation entropy (PMIPE). The simulation results and the verification results of real underwater acoustic signals indicate that our proposed method outperforms traditional methods in terms of accuracy, simplicity, and stability.

Deep learning‐based time delay estimation for motion compensation in synthetic aperture sonars

AbstractAccurate and robust time delay estimation is crucial for synthetic aperture sonar (SAS) imaging. A two‐step time delay estimation method based on displaced phase centre antenna (DPCA) micronavigation has been widely applied in motion estimation and compensation of SASs. However, the existing methods for time delay estimation are not sufficiently robust, which reduces the performance of SAS motion estimation. Deep learning is currently one of the cutting‐edge techniques and has brought about a remarkable progress in the field of underwater acoustic signal processing. In this study, a deep learning‐based time delay estimation method is introduced to SAS motion estimation and compensation. The subband processing is first applied to obtain ambiguous time delays between adjacent pings from phases of SAS echoes. Then, a lightweight neural network is utilised to construct phase unwrapping. The model of the employed neural network is trained with simulation data and applied to real SAS data. The results of time delay estimation and motion compensation demonstrate that the proposed neural network‐based method has much better performance than the two‐step and joint‐subband methods.

Line Spectrum Trajectory Detection Method of Underwater Acoustic Signal Based on Particle filter

Line spectrum detection is a significant task to detect, track, and classify weak target sources for passive sonar systems. In order to solve the problem of tracking time-varying frequency trajectories, a track-before-detect (TBD) algorithm based on the two-layer particle filter structure is proposed. The detect layer deals with the potential tracks by maintaining a low-threshold dynamic detection grid, which is used to determine the birth, death, and false alarm of targets. The tracking layer utilizes the particle filter to compute the joint multitarget probability density through a Bayesian framework. Simulation results show that the proposed algorithm can effectively detect and track time-varying frequency trajectories in a low signal-to-noise ratio situation, which has certain research and application value in weak signal detection.

Deep attention-based multi-task learning for underwater acoustic target recognition

Underwater acoustic target recognition is a key step in underwater acoustic signal processing, which is to judge the target attributes by extracting the features of the target radiated noise. However, the traditional feature extraction method is easily affected by the complex marine environment, resulting in a great decline in the correct recognition rate. In previous studies, the deep learning method was introduced to underwater acoustic target recognition and achieved good results. In this study, deep attention-based multi-task learning for underwater acoustic target recognition is proposed. This strategy trains encoder, decoder, and classifier in a multi-task framework to extract features that are more essential and suitable for classification. In addition, the attention mechanism is introduced in the process of feature extraction by the encoder, and the feature weight is given in the process of extraction, so as to improve the representation ability of the network. Experiments on measured underwater acoustic data are performed and the results show that this method can achieve a high accuracy rate in target recognition.

Underwater Acoustic Signal Noise Reduction Based on a Fully Convolutional Encoder-Decoder Neural Network

Underwater Acoustic Signal Noise Reduction Based on a Fully Convolutional Encoder-Decoder Neural Network

Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application

In recent years, underwater acoustic applications have attracted much attention, for example, for underwater environmental monitoring, underwater exploration, etc. Hydrophones play a particularly important role. Although hydrophone design has been in multifarious application forms, it still needs to consider increasing demand for low-cost, low-consumption, and multiple-function devices, as well as issues around miniaturization, lossless data collection, etc. In this paper, we design a compact underwater acoustic device that has the capability of underwater acoustic signal storage, underwater acoustic signal transmission via the Internet, and decoding based on the direct sequences spread spectrum (DSSS). The key problem is how to implement multiple functions in only one micro-controller unit (MCU). The hardware and software of the proposed multi-function hydrophone are described in detail. In particular, the MCU, the pre-amplifier with gain control, and the analog-to-digital integrated chip are introduced. Moreover, underwater acoustic data storage, underwater acoustic transmission, and the DSSS receiver are depicted in terms of software. The different functions of the hydrophone are verified in sea trial experiments. The results show that the proposed multi-function hydrophone is able to sample underwater acoustic data at high quality. In addition, to demonstrate configurable parameters, the DSSS receiver with different carrier frequencies is provided. The proposed multi-function hydrophone realizes zero bit error rate (BER) when carrier frequency fc=9 kHz, and the BER with 10−3 order of magnitude when carrier frequency fc=15.5 kHz. The results show that the proposed multi-function hydrophone has great potential to explore the ocean.

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.

Direct air–water communication by using an optical-acoustic method

The Air-Water Cross-Medium Communication (AWCMC) is significant for ocean exploration. This work presents an approach to direct AWCMC based on the optoacoustic conversion technique, which utilizes a pulsed laser and a distributed feedback fiber laser (DFB-FL) hydrophone. On-off keying (OOK) is used to modulate the pulsed laser, and ASCII is selected as the signal coding format. The light signal after modulation is transmitted to the air–water interface, where it is converted into underwater sound signals by the optoacoustic effect. Next, the underwater acoustic signal containing the encoded information is detected and demodulated by the DFB-FL hydrophone, which proves the feasibility of the proposed method used for the AWCMC. The experimental results indicate that the ASCII-encoded OOK signals with varying repetition frequencies can be generated by modulating the laser. Then, the performance of laser-generated sound (LGS) communication is evaluated by measuring the data rate and bit error rate (BER) with the repetition frequency in the range of 1–500 Hz. It is found that both the data rate and BER exhibit a positive correlation with the repetition frequency of the pulsed laser. Therefore, when optoacoustic technology is applied to the AWCMC, optimization design is required for the indicators of data rate and BER.

Research on Feature Extraction of Underwater Acoustic Target Radiation Noise Based on Machine Learning Algorithm

Underwater acoustic target recognition is a very important technology in the field of underwater acoustics, with great economic and military value. Feature extraction technology for underwater acoustic target radiation noise signals is the key to achieving acoustic target recognition. This study aims at the feature extraction task of acoustic targets and extracts 10 types of 252-dimensional feature vectors from three domains: time domain, frequency domain, and auditory domain. Through 7 machine learning algorithms for classification and recognition experiments, the experimental results show that the recognition performance of the ensemble classifier is much better than that of a single classifier. For different types of features, this study combines three ensemble learning algorithms and feature selection algorithms to select the original 252-dimensional features. The feature selection experiment shows that the wrapper feature selection algorithm has the best effect, and the feature vector dimension can be reduced to 40 dimensions. The recognition accuracy rate is not less than 92.8%, which provides feature extraction guidance for acoustic target recognition based on feature extraction.

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.

Laser-induced sonar: A promising approach for improved underwater acoustic sensing

This study presents the characteristics of acoustic pressure impulses generated by nanosecond laser-induced filamentation (ns-LIF) of water, focusing on the spatial, temporal, and spectral domains. In the time domain, the peak-to-peak (Pk-Pk) overpressures increase with higher incident optical energy while the arrival time remains constant. Notably, linearly polarized pulses exhibit higher Pk-Pk overpressures than circularly polarized pulses. Acoustic measurements of ns-LIF in water demonstrate a linear correlation between filament size and incident laser energy due to multiple plasma sources along the optical beam propagation. Alongside the temporal information, the spectrogram visualizes broad-spectrum underwater acoustic pressure impulses ranging from 10 to 800 kHz, perpendicular to the optical beam propagation. The low-frequency instantaneous underwater acoustic signals generated by ns-LIF is ∼90 kHz, offering advantages such as extended propagation distances and reduced attenuation in water. In addition to the experimental investigation, finite element analysis is employed to visualize the propagation and interaction of underwater acoustic signals across various interfaces. This integrated approach provides valuable insights into the behavior and characteristics of underwater signals. Eventually, our findings demonstrate the successful development of remote laser-induced sonar technology.

Multipath time delay estimation based on the improved Wigner-ville distribution

The accuracy of underwater multipath channel time delay is crucial in underwater acoustic signal processing. The temporal resolution of the traditional matched filter is restricted by the transmitted signal bandwidth. Wigner-Ville distribution (WVD) which has excellent energy concentration characteristics and effectively avoids the interaction between temporal and frequency resolutions, and is widely used in non-stationary signal processing. However, considering the multipath signal processing, the cross-term in echo WVD would cause inaccurate time delay estimation. Therefore, in this paper, a novel multipath signal time delay estimation method based on the improved WVD is proposed. By utilizing the difference in the continuity of energy changes between auto-term and cross-term in the time-frequency domain, the Seeded Region Growing algorithm is applied and improved to extract auto-term in the WVD. The simulation results show that the method removes cross-term and maintains high concentration characteristics of auto-term, compared with pseudo-WVD and smooth pseudo-WVD, etc. In addition, it improves the accuracy of multipath signal delay estimation and has some anti-noise ability.

Research on Embedded Multichannel Audio Conversion Module

With the rapid development and progress of information technology and the wide usage of audio signal processing in underwater acoustic signal processing, acoustic audio signal acquisition, conversion, and transmission technology always play an important role. To enhance the ability of signal acquisition and conversion with high reliability, this paper designs an embedded multi-channel audio conversion module. The module achieves multi-channel, multi-sample rates with synchronous analog-to-digital/digital-to-analog conversion (ADC/DAC) function, and additionally equips with dual network redundancy. The module uses a chip integrated with ARM and programmable logic FPGA as the main control chip. The writing of the underlying driver and application code ensures the reliable operation of the DAC chip and ADC chip and the steady of the network transmission. Through experimental verification, the audio conversion module performs well in multi-channel and multi-sample rates with synchronous ADC/DAC, and the crosstalk between channels is less than -50 dB. The dual network redundancy design and the application of the RS232 serial port ensure the high fidelity, reliability, and reprogramming of the audio conversion function, which shows the design can be used in a wide range of scenarios.

Data-Driven DOA Estimation Methods Based on Deep Learning for Underwater Acoustic Vector Sensor Array

Abstract Direction of arrival (DOA) estimation is a fundamental problem in underwater acoustic vector sensor array signal processing. Because of the advantages of deep learning technology, this paper proposes two categories of data-driven DOA estimation methods for underwater acoustic vector sensor array, which transform the DOA estimation problem into a neural network classification problem. Specifically, one is the DOA estimation method of convolutional neural network based on teacher-student noise reduction (TS-CNN), which considers the covariance matrix as the training data set; the other is a DOA estimation method based on long-short term memory network and attention mechanism (LSTM-ATT), which applies the time-domain signal as the training data set. The experimental simulation results show that: 1) when the number of array elements is small, the accuracy of the DOA estimation method based on TS-CNN is equivalent to that of traditional methods, and it can effectively suppress the influence of noise when the signal-to-noise ratio (SNR) is low; 2) the accuracy of DOA estimation method based on LSTM-ATT is much higher than that of traditional Multiple Signal Classification method, especially in the case of low SNR, which also proves the importance of temporal characteristics for DOA estimation in a real environment.

An attention-based multi-scale convolution network for intelligent underwater acoustic signal recognition

Underwater acoustic signal identification is always an extremely challenging task due to the intricate marine environments and background noise interference. Existing techniques typically focus on handling only one type of specific features for recognition, overlooking a comprehensive representation of distinct vessel attributes and struggling to handle intricate scenarios. To address these challenges, we employ a band-pass filter bank for feature extraction. This process extracts multiple representations of the same data, each concentrating on distinct frequency components. Subsequently, the filtered multi-view representation is channeled into an attention-based multi-scale convolution network (AMSC-Net) for recognition purposes. AMSC-Net integrates a sequence of attention-based multi-scale convolution (AMSC) blocks to capture pivotal features tied to identifying diverse ship-radiated audio categories. Specifically, a set of parallel residual convolution layers with different kernels are designed to capture multi-scale features, and an adaptive channel attention module is followed to highlight dominant parts within global features. To enrich training data in terms of quantity and diversity, a data augmentation strategy is implemented during the training phase. Our proposals are evaluated on two real-world underwater acoustic signal datasets. Experimental results firmly establish the superiority of our proposed AMSC-Net, which substantially outperforms competitive DNN-based methods. Moreover, our proposals exhibit robustness across different noise levels, achieving an overall accuracy of 95.87% and 95.39% for the two datasets at 0 dB signal-to-noise ratio (SNR), respectively.

Energy-distributable waterborne acoustic launcher for directional sensing

Highly directional launch and intensity adjustment of underwater acoustic signals are crucial in many areas such as abyssal navigation, underwater signal communication, and detection for marine biology. Inspired by the phenomenon that aquatic animals like dolphins detect and track prey with high resolution, we propose an energy-distributable directional sensing strategy which can achieve parallel needle-like transmitting sound beams with adjustable energy based on out-coupling valley-polarized edge states. The acoustic spin angular momentum and energy flow distribution at different interfaces inside the phononic crystal are provided and they show tight coupling. Furthermore, a sound beam with a width of 20° and an acoustic intensity enhancement factor ≈ 6.6 are observed in the far field. As an application, we show that this device can be used as an acoustic energy distributor. This communication pattern with excellent functionalities and performance provides a desirable idea for high-energy-level directional collimated underwater sensing and underwater acoustic energy distribution.

A Modulation Recognition System for Underwater Acoustic Communication Signals Based on Higher-Order Cumulants and Deep Learning

Underwater acoustic channels, influenced by time-varying, space-varying, frequency-varying, and multipath effects, pose significant interference challenges to underwater acoustic communication (UWAC) signals, especially in non-cooperative scenarios. The task of modulating and identifying distorted signals faces huge challenges. Although traditional modulation recognition methods can be useful in the radio field, they often prove inadequate in underwater environments. This paper introduces a modulation recognition system for recognizing UWAC signals based on higher-order cumulants and deep learning. The system achieves blind recognition of received UWAC signals even under non-cooperative conditions. Higher-order cumulants are employed due to their excellent noise resistance, enabling the differentiation of OFDM signals from PSK and FSK signals. Additionally, the high-order spectra differences among signals are utilized for the intra-class recognition of PSK and FSK signals. Both simulation and lake test results substantiate the effectiveness of the proposed method.