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The underwater acoustic (UWA) channel causes large propagation delays and reduces the bit error rate (BER) of wireless communication systems. The t-distribution is the optimal distribution to perform UWA noise. In this study, polar-coded differential chaos shift keying (DCSK) and quadrature chaos shift keying (QCSK) communication with UWA noise are considered. First, we have proposed a PDF for the UWA noise channel, and based on this PDF, the theoretical BER is derived. Second, polar coding’s performance is determined to demonstrate the improvement in the BER performance compared to the uncoded UWA system by means of Monte Carlo simulations. The experimental results prove that the nearest model that is applicable to the UWA channel is a t-distribution with five and six degrees of freedom. The BER formulas of the proposed systems are derived and compared with the simulation results. The results confirm the performance improvement of the polar-coded chaotic modulation systems over uncoded systems in UWA channels.

Underwater communication is one of the most important and difficult challenges facing researchers due to the high attenuation of the signal, communication with the surface because of the harsh medium of water, and data transmission performance degradation as a result of various effects. Underwater acoustic communication (UWA) has a low data rate, which describes the disadvantage of this type of communication. In addition, it has a low bandwidth range and high latency but has a long transmission range as an advantage. Multicarrier wireless transmission systems increase the data rate by sending the data using more than one carrier. We proposed a noncoherent orthogonal frequency division multiplexing (OFDM) method to increase the data rate in UWA communication systems. In addition, doubling the data rate in the OFDM using Subcarrier Power Modulation (OFDM-SPM) system can save half of the bandwidth. The MATLAB simulation program was used to implement the system in the underwater acoustic environment to increase its throughput. The proposed design uses Differential Phase Shift Keying (DPSK) with power control, and the data stream is transmitted through two-dimensional modulation schemes, the DPSK, and the power level of each subcarrier in the OFDM system with cyclic prefix (CP). The underwater channel was designed using a Rician fading multipath with a spreading loss formula as a function of distance and frequency. We designed an equalizer at the receiver side to recover the original signal as a function of three parameters which are: the channel effect as a rate between transmitting and receiving symbols, the Rician channel response, and the UWA spreading loss. OFDM-Subcarrier Power Modulation (OFDM-SPM) using the proposed equalizer performed better than the theoretical OFDM-SPM in the Rayleigh channel. The designed equalizer increased the performance of the OFDM-SPM system by 25% which helped to enhance the system's throughput and doubled the data rate compared with the OFDM system, doubling the data rate using OFDM-SPM had been validated in laboratory experiments in the Time domain. [ABSTRACT FROM AUTHOR]

Ship-radiated noise separation is critical in both military and economic domains. However, due to the complex underwater environments with multiple noise sources and reverberation, separating ship-radiated noise poses a significant challenge. Traditionally, underwater acoustic signal separation has employed blind source separation methods based on independent component analysis. Recently, the separation of underwater acoustic signals has been approached as a deep learning problem. This involves learning the features of ship-radiated noise from training data. This paper introduces a deep time-domain network for ship-radiated noise separation by leveraging the power of parallel dilated convolution and group convolution. The separation layer employs parallel dilated convolution operations with varying expansion factors to better extract low-frequency features from the signal envelope while preserving detailed information. Additionally, we use group convolution to reduce the expansion of network size caused by parallel convolution operations, enabling the network to maintain a smaller size and computational complexity while achieving good separation performance. The proposed approach is demonstrated to be superior to the other common networks in the DeepShip dataset through comprehensive comparisons. [ABSTRACT FROM AUTHOR]

The underwater acoustic (UWA) channel causes large propagation delays and reduces the bit error rate (BER) of wireless communication systems. The t-distribution is the optimal distribution to perform UWA noise. In this study, polar-coded differential chaos shift keying (DCSK) and quadrature chaos shift keying (QCSK) communication with UWA noise are considered. First, we have proposed a PDF for the UWA noise channel, and based on this PDF, the theoretical BER is derived. Second, polar coding's performance is determined to demonstrate the improvement in the BER performance compared to the uncoded UWA system by means of Monte Carlo simulations. The experimental results prove that the nearest model that is applicable to the UWA channel is a t-distribution with five and six degrees of freedom. The BER formulas of the proposed systems are derived and compared with the simulation results. The results confirm the performance improvement of the polar-coded chaotic modulation systems over uncoded systems in UWA channels. [ABSTRACT FROM AUTHOR]

Underwater acoustic target recognition plays an essential role in sonar signal processing. Despite numerous efforts in target recognition, it remains a challenging task due to the complex nature of the underwater environment. Specifically, the overlapping acoustic feature of different target classes, coupled with the temporal-spatial variation characteristic of the marine environment, results in reduced recognition performance. This paper introduces a novel approach to address these challenges, which emphasizes the acquisition of fine-grained feature parameters and achieving high-precision classification results. Firstly, we develop a wavelet-auditory feature that comprehensively represents the underwater acoustic signal. The feature describes the time-frequency auditory information and reduces the dimensionality of the original signal. Secondly, a convolutional recurrent neural network with residual blocks module is designed, which enables the extraction of more discriminative global deep features from the wavelet-auditory feature. The network addresses the effects of time-varying and time-dependent nonuniform underwater environments. Finally, we conduct experiments on the ShipsEar database to evaluate the proposed method. Experimental results demonstrate that our method outperforms other classification methods in terms of recognition accuracy. Furthermore, the efficacity of each method component has been demonstrated via ablation studies, demonstrating that the proposed method is a significant initiative and contribution to the underwater acoustic target recognition task. [ABSTRACT FROM AUTHOR]

In the field of acoustic simulation, methods that are widely applied and have been proven to be highly effective rely on accurately capturing the impulse response (IR) and its convolution relationship. This article introduces a novel approach, named as UnderwaterImage2IR, that generates acoustic IRs from underwater images using dual‐path pre‐trained networks. This technique aims to achieve cross‐modal conversion from underwater visual images to acoustic information with high accuracy at a low cost. Our method utilizes deep learning technology by integrating dual‐path pre‐trained networks and conditional generative adversarial networks conditional generative adversarial networks (CGANs) to generate acoustic IRs that match the observed scenes. One branch of the network focuses on the extraction of spatial features from images, while the other is dedicated to recognizing underwater characteristics. These features are fed into the CGAN network, which is trained to generate acoustic IRs corresponding to the observed scenes, thereby achieving high‐accuracy acoustic simulation in an efficient manner. Experimental results, compared with the ground truth and evaluated by human experts, demonstrate the significant advantages of our method in generating underwater acoustic IRs, further proving its potential application in underwater acoustic simulation. [ABSTRACT FROM AUTHOR]

In this study, deep learning network technology is employed to solve the problem of rapid changes in underwater channels. The modulation techniques employed are frequency-shift keying (FSK) and the BELLHOP module of MATLAB; they are used to create water with multipath, Doppler shifts, and additive Gaussian white noise such that underwater acoustic receiving signals simulating the actual ocean environment can be obtained. The southwest coastal area of Taiwan is simulated in the manuscript. The results reveal that optimizing the environment by using the virtual time reversal mirror (VTRM) technique can generally mitigate the bit error rates (BERs) of the deep learning network's model receiver and traditional demodulation receiver. Lastly, seven deep learning networks are deployed to demodulate the FSK signals, and these approaches are compared with traditional demodulation techniques to determine the deep learning network techniques that are most suitable for marine environments. [ABSTRACT FROM AUTHOR]

Thornfish (Terapon jarbua) is a significantly commercial species inhabiting the shallow coastal waters of the Indo-Pacific Ocean. To achieve effective underwater acoustic (UWA) monitoring on the abundance and population dynamics of this species, the comprehensive target strength (TS) characteristics should be investigated and understood. In this study, the Kirchhoff-ray mode (KRM) model was adopted to evaluate and analyze the acoustic TS of T. jarbua and its variations with the sound wave frequency, pitch angle distributions as well as morphological characteristics in the South China Sea. A total of 19 samples were captured and evaluated at four types of frequencies of 38 kHz, 70 kHz, 120 kHz, and 200 kHz. The results demonstrated that the TS of T. jarbua varied with the pitch angle shifts, and the number of secondary TS peaks increased as the increasing frequency accordingly. Two classic pitch angle distributions that included N[−5°, 15°] and N[0°, 10°] were adopted to calculate the average TS of T. jarbua. The fitted TS-L regression formulations and the standard b20 form equations were determined at different pitch angle distributions as well as frequencies. These results could support the accurate and reliable UWA abundance estimation in the South China Sea to facilitate a better understanding of the abundance and population dynamics of T. jarbua. [ABSTRACT FROM AUTHOR]

The classification of underwater acoustic targets is a challenging task due to the intricacies involved in soundscape, huge amounts of background interference, and the varied features of underwater targets. Various approaches have been explored for classifying underwater targets based on acoustic signatures. The problem with these conventional methods lies in the extensive domain-specific knowledge they demand for effective feature engineering. Alternatively, the deep learning approach is of great appeal for assistance to manual sonar operators who mainly rely on their expertise for the classification of targets. This work leverages the approach of deep neural networks for underwater acoustic target categorization, aiming to analyze whether we can reduce the need for extensive domain knowledge required, by letting deep learning algorithms itself find the deep audio embeddings. This approach implements an audio classifier designed to classify acoustic targets with input as log Mel-spectrograms. To compare the performance of this classifier, we also implement a regular neural network for audio classification with MFCCs as input features. The overall results of the model are promising. [ABSTRACT FROM AUTHOR]

In UnderWater-Acoustic-Orthogonal-Frequency-Division-Multiplexing-(UWA-OFDM) communication, the traditional interpolated channel estimation method produces error codes, due to the small number of user pilots, uneven distribution, and complex channel characteristics. In this paper, we propose a novel UWA-channel-estimation method based on Deep Learning (DL). First, based on a small number of channel samples, we used the CWGAN-GP model to generate enhanced classified underwater-acoustic channel samples to have semantic similarity to the real samples and also to present the diversity of the samples. After obtaining the channel sample, the pilot estimation matrix was processed in a similar image way. Here, we extracted the channel features by constructing a convolutional network structure similar to U-Net, weakening the impact of feature information loss. A Channel-Attention-Denoising-(CAD) module was also designed, to further optimize the reconstructed channel information. The simulation results verified the superiority of the proposed algorithm, in terms of Mean Square Error (MSE) and Bit-Error Rate (BER) compared to the existing Least-Squares-(LS), Deep-Neural-Network-(DNN), and ChannelNet algorithms. [ABSTRACT FROM AUTHOR]

With current developments, underwater communication using acoustic signals is widely used. Many things need to be prepared to support a reliable underwater communication system, such as taking measurements in a test tank to find out the correct measurement configuration. Underwater acoustic intensity measurements, which are detailed in this paper, are performed in the test tank using distance variation schemes. Measurements were made at various distances of 4, 10, 20, and 50 meters from the signal source. The hydrophone that was used has a sensitivity of -180 dB re 1V/µPa. The hydrophone was placed at a depth of 2 meters below the surface of the water in the test tank, which divided the test tank depth in half to ensure that reflections from the bottom and the surface were kept to a minimum. However, the problem is that there are noisy signals at different frequencies. This paper proposes a method using Noise Assisted - Multivariate Empirical Mode Decomposition (NA-MEMD) to decompose the signal and then calculate the sound intensity. The result shows that an increase in the distance between the transmitter and receiver, also causes a change in the intensity with an average change of 0.467 dB/meter. It is concluded that the NA-MEMD approach was shown to be successful in decomposing the intended signal from the noise to equalize the quality of the signal received at different distances, and the correlation between intensity value and change in distance is resilient, with a correlation value of 0.98, indicating a very strong correlation.

This paper applies topology optimization in combination with a genetic algorithm to design a coating structure for sound absorption in underwater applications. The coating is designed with a high broadband acoustic absorption of 0.94 ranging from 1 to 10 kHz by optimizing the cavity topology and the dimensions of the embedded steel scatterers. This study demonstrates the application process of the topology optimization with a genetic algorithm method and the optimization results of the optimization process stages. The absorption mechanism of the topology design is analyzed. The effects of different cavity compositions, steel scatterers, design ideas, material parameters, and acoustic incident angles on the sound absorption coefficients are investigated. These acoustic structures and the method provide more ideas for the design of coating structures for underwater sound absorption. [ABSTRACT FROM AUTHOR]

In-Band Full-Duplex (IBFD) systems have the capability of simultaneously transmitting and receiving signals through the channel and require the same resources as half-duplex systems. Unfortunately, IBFD systems have self-interference (SI) issues that prevent the system from gaining double throughput with respect to half-duplex systems. Therefore, the IBFD system will be more reliable if SI is mitigated more. This contribution will look at SI cancellation in wireless radio and underwater acoustic systems. The reviewed documents cover alltypes of SI cancellations, including passive, analog, and digital cancellations. In a practical full-duplex system, the SI cancellation for all domains must cancel the SI below the receiver noise floor. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ship-radiated noise separation is critical in both military and economic domains. However, due to the complex underwater environments with multiple noise sources and reverberation, separating ship-radiated noise poses a significant challenge. Traditionally, underwater acoustic signal separation has employed blind source separation methods based on independent component analysis. Recently, the separation of underwater acoustic signals has been approached as a deep learning problem. This involves learning the features of ship-radiated noise from training data. This paper introduces a deep time-domain network for ship-radiated noise separation by leveraging the power of parallel dilated convolution and group convolution. The separation layer employs parallel dilated convolution operations with varying expansion factors to better extract low-frequency features from the signal envelope while preserving detailed information. Additionally, we use group convolution to reduce the expansion of network size caused by parallel convolution operations, enabling the network to maintain a smaller size and computational complexity while achieving good separation performance. The proposed approach is demonstrated to be superior to the other common networks in the DeepShip dataset through comprehensive comparisons.

Nonlinear acoustics is a critical area of study with practical applications in fields such as underwater communications, medical imaging, non-destructive testing, and sonar. This paper offers a comprehensive analysis of the Westervelt and Burgers equations, along with their related boundary problems, and investigates the characteristics of parametric generation, thereby making substantial advancements in the theoretical comprehension of nonlinear acoustic waves. Our analysis sheds new light on the dynamics of nonlinear acoustic waves and their behavior in various media, providing valuable insights into the physics of sound propagation. Finally, parametric effects can be intelligently exploited for communication applications. Thus, through the appropriate selection of encodings, it is possible to develop underwater acoustic communication systems with greater directivity and range than classical systems.

The use of coherent broadband signals instead of explosive, high-intensity air-gun pulses is one potential way to reduce sound pressure and sound exposure levels in marine surveys. A coherent type of seismic source achieves a higher resolution, hence a sharper image of deep layers. In the upper-frequency range of seismic signals (higher than 16 Hz), marine vibrators can successfully apply the known acoustic technology of low-frequency underwater transducers. This technology is not as effective at low seismic frequencies. The slow progress in the development of this technology requires finding a different approach. An alternative would be to employ a gas-filled underwater cylindrical bubble resonator. A large gas-filled resonance bubble covered with an elastic membrane and driven by a blower with airflow controlled by a proportional valve provides significant power in the low-frequency range. We achieved a suitable broadband frequency response by designing a double resonance system consisting of a bubble and an internal Helmholtz gas acoustic resonator. Experimental tests were conducted at the Woods Hole Oceanographic Institution. This article provides an overview of engineering solutions considered in the design, including drivers, membranes, and methods of increasing efficiency. [ABSTRACT FROM AUTHOR]

In the marine environment, estimating the direction of arrival (DOA) is challenging because of the multipath signals and low signal-to-noise ratio (SNR). In this paper, we propose a convolutional recurrent neural network (CRNN)-based method for underwater DOA estimation using an acoustic array. The proposed CRNN takes the phase component of the short-time Fourier transform of the array signals as the input feature. The convolutional part of the CRNN extracts high-level features, while the recurrent component captures the temporal dependencies of the features. Moreover, we introduce a residual connection to further improve the performance of DOA estimation. We train the CRNN with multipath signals generated by the BELLHOP model and a uniform line array. Experimental results show that the proposed CRNN yields high-accuracy DOA estimation at different SNR levels, significantly outperforming existing methods. The proposed CRNN also exhibits a relatively short processing time for DOA estimation, extending its applicability.

The in-band full-duplex underwater acoustic communication (IBFD-UWAC) mode has twice the information throughput of the traditional half-duplex communication mode, significantly increasing the communication efficiency. Extracting the weak desired signal from the high-power self-interference signal without distortion remains a challenging problem in implementing IBFD-UWAC systems. This paper proposes a spatial-digital joint self-interference cancellation (SDSIC) method for IBFD-UWAC. We first perform spatial self-interference cancellation (SSIC) and propose an improved wideband constant-beamwidth beamformer to overcome the problem of direction- and array-dependent interference in IBFD-UWAC systems. Convex optimization is used to maintain a constant beam response in the main flap and cancel the self-interference signal from a fixed direction, thus increasing the signal-to-interference ratio of the desired signal. Subsequently, we perform digital self-interference cancellation (DSIC) on the residual self-interference signal, and propose a variable-step-size least-mean-squares algorithm based on the spatial noise threshold. This algorithm modifies the least-mean-squares step-size adjustment criterion according to the noise level after SSIC and the desired signal, resulting in better DSIC. A series of simulations are implemented in a hardware-in-the-loop platform to verify the practicality and real-time performance of the proposed SDSIC method. The results show that the self-interference signal power can be reduced by 41.5 dB using the proposed method, an improvement of 13.5 dB over the conventional SIC method.

Bit error rate (BER) is typically high in underwater acoustic (UWA) channel, which is characterized by high propagation delay and poor quality of communications. UWA noise statistics do not follow the standard Gaussian distribution. It has been proven through field tests that the noise follows the t-distribution in Malaysian shallow-water. In this paper, a study on UWA error performance is presented based on t-distribution. Furthermore, the expressions of error performance are derived using binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulations order. Moreover, the new waveform filtered orthogonal frequency division multiplexing (F-OFDM) in UWA with turbo and convolution code is adopted. The simulation results show that at BER 10–3, the Signal-to-Noise Ratio (SNR) is 6 dB and 11 dB for BPSK and QPSK, respectively. The turbo code performance appears to be superior over the convolution code. Furthermore, the results indicate that F-OFDM significantly improves the power spectral density to approximately 120 dBW compared with OFDM. [ABSTRACT FROM AUTHOR]

The performance of the coded generalized frequency division multiplexing (GFDM) transceiver has been evaluated in a shallow underwater acoustic channel (UAC). Acoustic transmission is the scheme of choice for communication in UAC since radio waves suffer from absorption and light waves scatter. Although orthogonal frequency division multiplexing (OFDM) has found its ground for multicarrier acoustic underwater communication, it suffers from high peak to average power ratio (PAPR) and out of band (OOB) emissions. We propose a coded-GFDM based multicarrier system since GFDM has a higher spectral efficiency compared to a traditional OFDM system. In doing so, we assess two block codes, namely Bose, Chaudari, and Hocquenghem (BCH) codes, Reed-Solomon (RS) codes, and several convolutional codes. We present the error performances of these codes when used with GFDM. Furthermore, we evaluate the performance of the proposed system using two equalizers: Matched Filter (MF) and Zero-Forcing (ZF). Simulation results show that among the various block coding schemes that we tested, BCH (31,6) and RS (15,3) give the best error performance. Among the convolutional codes that we tested, rate 1/4 convolutional codes give the best performance. However, the performance of BCH and RS codes is much better than the convolutional codes. Moreover, the performance of the ZF equalizer is marginally better than the MF equalizer. In conclusion, using the channel coding schemes with GFDM improves error performance manifolds thereby increasing the reliability of the GFDM system despite slightly higher complexity. [ABSTRACT FROM AUTHOR]

Underwater acoustic target recognition is one of the main functions of the SONAR systems. In this paper, a target recognition method based on combined features with automatic coding and reconstruction is proposed to classify ship radiated noise signals. In the existing underwater acoustic target recognition systems, the target category features are mostly constructed based on the power spectrum according to a certain presupposed model, and some useful information in the data is discarded artificially. In the proposed recognition method, a feature extractor based on auto-encoding is designed. The feature extractor uses the restricted Boltzmann machine (RBM) to automatically encode the combined data of the power spectrum and demodulation spectrum of ship radiated noise without supervision and extracts the deep data structure layer by layer to obtain the signal feature vector. The extracted feature vector is sent to a Back Propagation (BP) neural network to realize target recognition. Due to the high cost of ship radiated noise acquisition, the sample size of ship radiated noise signals is often hard to meet the needs of neural network training. A method of data augmentation is designed by RBM auto-encoder to construct the expanded sample set, which improves the performance of the recognition system. The experimental results based on the actual ship’s radiated noise show that the proposed method has better performance than the traditional methods.

The research domain of underwater communication has garnered much interest among researchers exploring underwater activities. The underwater environment differs from the terrestrial setting. Some of the main challenges in underwater communication are limited bandwidth, low data rate, propagation delay, and high bit error rate (BER). As such, this study assessed the underwater acoustic (UWA) aspect and explored the expression of error performance based on t‐distribution noise. Filter orthogonal frequency‐division multiplexing refers to a new waveform candidate that has been adopted in UWA, along with turbo and polar codes. The empirical outcomes demonstrated that the noise did not adhere to Gaussian distribution, whereas the simulation results revealed that the filter applied in orthogonal frequency‐division multiplexing could significantly suppress out‐of‐band emission. Additionally, the performance of the turbo code was superior to that of the polar code by 2 dB at BER 10−3.

This paper proposes Snapping Shrimp Noise (SSN) detection and corrupted Orthogonal Frequency Division Multiplexing (OFDM) reconstruction methods to increase Bit Error Rate (BER) performance when OFDM transmitted signal is corrupted by impulsive SSNs in underwater acoustic communications. The proposed detection method utilizes multilayer wavelet packet decomposition for detecting impulsive and irregularly concentrated and SSN energy in specific frequency bands of SSN, and the proposed reconstruction scheme uses iterative decision directed-subcarrier reconstruction to recover corrupted OFDM signals using multiple carrier characteristics. Computer simulations were executed to show receiver operating characteristics curve for the detection performance and BER for the reconstruction. The practical ocean experiment of SAVEX 15 demonstrated that the proposed method exhibits a better detection performance compared with conventional detection method and improves BER by 250% and 1230% for uncoded and coded data, respectively, compared with the conventional reconstruction scheme.

With the increasingly fierce competition for marine resources, underwater acoustic communication as the main form of underwater communication, its security has received more and more attention. The traditional underwater acoustic communication technology with fixed frequency and modulation may cause information leakage and location exposure of the communicating platform. The communication based on ocean noise is helpful to improve the communication security. This article proposes a new covert underwater acoustic communication scheme based on ship-radiated noise and chaos signal. Firstly, the characteristics of chaos signal is studied, which is generated by the Chebyshev sequence, and the reconstruction process of ship-radiated noise is analyzed. Secondly, we generate mimic ship-radiated noise containing the secret message based on the time and frequency feature of the chaos signal and ship-radiated noise. Finally, resampling technology is used to equalize the Doppler frequency shift, and time inversion mirror technology is used to perform multipath channel equalization. Simulation and sea experiment verify the effectiveness and feasibility of this scheme. In the sea experiments, the proposed scheme can reliably transmit over 40 per second when the communication distance is about 10km.

In this work, the spatial correlation of a multiple-input multiple-output (MIMO) for underwater acoustic (UWA) channel is modelled. To obtain the spatial correlation for such a channel, a mathematical method to model the effect of the surface on the acoustic propagation is studied. The sea surface has a significant impact on the underwater acoustic propagation (UWA) channel since the sound field is scattered, particularly in rough sea conditions. In a situation where the sea surface is calm, the reflection is specular. In contrast, a sea surface subject to high sea states generates scattered waves. In these conditions, more complex mathematical equations are required to model the propagation. Current analytical models have limitations in terms of complexity and are not practical. Therefore, this study aims to consider a specular reflection to model the time-varying sea surface on the UWA channel. It is a simple model with low computationally complexity and can be used to assess the performance of UWA communications. Specifically, the specular reflection and transmission of an acoustic wave at a calm sea surface is studied, using the Huygens-Fresnel principle and the superposition theorem. The analytical model is developed using physical oceanic parameters representing the sea conditions. The results show a good agreement with the experimental analysis.

The research domain of underwater communication has garnered much interest among researchers exploring underwater activities. The underwater environment differs from the terrestrial setting. Some of the main challenges in underwater communication are limited bandwidth, low data rate, propagation delay, and high bit error rate (BER). As such, this study assessed the underwater acoustic (UWA) aspect and explored the expression of error performance based on t‐distribution noise. Filter orthogonal frequency‐division multiplexing refers to a new waveform candidate that has been adopted in UWA, along with turbo and polar codes. The empirical outcomes demonstrated that the noise did not adhere to Gaussian distribution, whereas the simulation results revealed that the filter applied in orthogonal frequency‐division multiplexing could significantly suppress out‐of‐band emission. Additionally, the performance of the turbo code was superior to that of the polar code by 2 dB at BER 10−3. [ABSTRACT FROM AUTHOR]

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The potential benefits of massive multiple-input multiple-output (MIMO) make it possible to achieve high-quality underwater acoustic (UWA) communications. Nevertheless, due to the wideband nature of UWA channels, existing massive MIMO techniques for radio frequency cannot be directly applied to UWA communications. This paper investigates a UWA massive MIMO system in the shallow-water environment, deploying large array apertures at both the transmitter and the receiver. We propose a beam-based UWA massive MIMO channel model and analyze its properties. Based on this model, we reveal that the transmit design for rate maximization can be performed in a dimension-reduced space related to the channel taps. Then, we prove that the beam-domain transmission is optimal to maximize the rate when with unlimited numbers of transducers. Furthermore, if the number of hydrophones also tends to infinity, the optimal power allocation can be obtained just by the water-filling algorithm and the corresponding rate positively correlates with the number of channel taps for the high signal-to-noise-ratio regime. Moreover, we devise a low-complexity algorithm to optimize the input covariance matrix for general cases. Simulation results illustrate the significant performance of the proposed algorithm and the high throughput achieved by massive MIMO. [ABSTRACT FROM AUTHOR]

The performance of the coded generalized frequency division multiplexing (GFDM) transceiver has been evaluated in a shallow underwater acoustic channel (UAC). Acoustic transmission is the scheme of choice for communication in UAC since radio waves suffer from absorption and light waves scatter. Although orthogonal frequency division multiplexing (OFDM) has found its ground for multicarrier acoustic underwater communication, it suffers from high peak to average power ratio (PAPR) and out of band (OOB) emissions. We propose a coded-GFDM based multicarrier system since GFDM has a higher spectral efficiency compared to a traditional OFDM system. In doing so, we assess two block codes, namely Bose, Chaudari, and Hocquenghem (BCH) codes, Reed-Solomon (RS) codes, and several convolutional codes. We present the error performances of these codes when used with GFDM. Furthermore, we evaluate the performance of the proposed system using two equalizers: Matched Filter (MF) and Zero-Forcing (ZF). Simulation results show that among the various block coding schemes that we tested, BCH (31,6) and RS (15,3) give the best error performance. Among the convolutional codes that we tested, rate 1/4 convolutional codes give the best performance. However, the performance of BCH and RS codes is much better than the convolutional codes. Moreover, the performance of the ZF equalizer is marginally better than the MF equalizer. In conclusion, using the channel coding schemes with GFDM improves error performance manifolds thereby increasing the reliability of the GFDM system despite slightly higher complexity.

We propose an effective, low complexity and multifaceted scheme for peak-to-average power ratio (PAPR) reduction in the orthogonal frequency division multiplexing (OFDM) system for underwater acoustic (UWA) channels. In UWA OFDM systems, PAPR reduction is a challenging task due to low bandwidth availability along with computational and power limitations. The proposed scheme takes advantage of XOR ciphering and generates ciphered Bose–Chaudhuri–Hocquenghem (BCH) codes that have low PAPR. This scheme is based upon an algorithm that computes several keys offline, such that when the BCH codes are XOR-ciphered with these keys, it lowers the PAPR of BCH-encoded signals. The subsequent low PAPR modified BCH codes produced using the chosen keys are used in transmission. This technique is ideal for UWA systems as it does not require additional computational power at the transceiver during live transmission. The advantage of the proposed scheme is threefold. First, it reduces the PAPR; second, since it uses BCH codes, the bit error rate (BER) of the system improves; and third, a level of encryption is introduced via XOR ciphering, enabling secure communication. Simulations were performed in a realistic UWA channel, and the results demonstrated that the proposed scheme could indeed achieve all three objectives with minimum computational power.

Pre-weakening of solid materials using electric discharge is a new technique aiming at reducing significantly the costs and energy consumption as compared with the traditional raw materials processing in mining and recycling industries. However, the absence of an effective pre-weakening process monitoring and control prohibits its introduction into the market. The present contribution aims to fill this gap by investigating the feasibility of combining acoustic emission with machine learning for process monitoring. Hence, this paper is a supplement and enrichment of existing studies on in situ and real-time process monitoring and diagnosis associated with failure mechanism problems. Three categories and six subcategories are defined to describe the major pre-weakening scenarios of solid materials. The acoustic signals are collected and labeled according to the visual control of specially prepared transparent samples subjected to discharge exposure. The acoustic signals are decomposed with data adaptive M-band wavelets and the relative energies of the extracted frequency bands are used as features. Principal component analysis is applied to select the most informative features whereas several classifiers are applied to recognize the pre-weakening quality. The classification accuracy of the defined categories ranges between 84-93% demonstrating the applicability of the proposed method for in situ and real-time control of pre-weakening of solid materials using electric discharge.

Time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) has been attractive due to its fast synchronization and efficient spectral efficiency over conventional cyclic prefix orthogonal frequency division multiplexing (OFDM) and zero padding OFDM. However, inter-block interference (IBI) affects its performance because of delay over multipath channels. To evade IBI, dual pseudo-random noise (DPN) sequences have been introduced that causes to reduce spectral and energy efficiency. But, DPN is unprepared for underwater acoustic (UWA) communication because of battery-based nature and limited bandwidth. To overcome these issues, this paper exploits compressive sensing theory algorithm for obtaining the time-varying channel state information by utilizing sparse property of UWA channels. In this paper, the IBI free region is utilized to estimate accurate UWA channel impulse response and mitigate its interference. Look-ahead backtracking orthogonal matching pursuit-based sparse channel estimation technique is proposed for underwater TDS-OFDM in a real sparse time-varying multipath channel (channel taps are randomly distributed). Furthermore, Doppler-shift of UWA channel is estimated and compensated by PN sequence in time domain. The performance of the proposed technique is evaluated and demonstrated through numerical computation of bit error rate (BER) and mean square error (MSE) using Monte Carlo iterations. Simulation analysis confirms the superiority of the proposed scheme not only in terms of BER and MSE over the conventional ones but also achieve high energy and spectral efficiency.

In this study, the effect of the emergence angle of a source array on acoustic transmission in a typical shallow sea is simulated and analyzed. The formula we derived for the received signal based on the Normal Mode indicates that the signal is determined by the beamform on the modes of all sources and the samplings of all modes at the receiving depth. Two characteristics of the optimal emergence angle (OEA) are obtained and explained utilizing the aforementioned derived formula. The observed distributions of transmission loss (TL) for different sources and receivers are consistent with the obtained characteristics. The results of this study are valuable for the development and design of active sonar detection. [ABSTRACT FROM AUTHOR]

• Impedance is an essential ultrasonic property of materials. • Spherical waves produce a far-field close to the transducer surface. • A method using two transducers was proposed to assess ultrasonic impedance. • The results validated the method with relatively low uncertainty. The mechanical properties and composition of materials can be better understood through ultrasonic non-destructive testing. Ultrasound is a valuable tool for characterizing elastic moduli, material microstructure, morphological conditions, and correlated acoustical/mechanical properties. Although ultrasound measurement techniques like transmission and pulse-echo are well-established, there is a growing demand for new technologies to facilitate automated inspection and enhance the widespread application of this method. This paper presents a novel approach to evaluate the characteristic acoustic impedance of a sample using measurements of reflected spherical waves. Employing a spherical sound source makes the energy distribution more uniform in all directions, minimizing measurement errors caused by system alignment. Additionally, that configuration aids in determining the experiment's geometry. The impedance was evaluated by analysis of the spherically reflected Gaussian-modulated sinusoidal pulses, which are less susceptible to noise interference. The acquired signal is fitted into a mathematical model to calculate the acoustic impedance, considering the decrease in sound pressure amplitude at the spherical wavefront according to the inverse distance law. Materials with a wide range of impedances (2 MRayl < Z < 46 MRayl) were measured to validate the method. The experimental impedances exhibited statistically significant agreement with values obtained via through-transmission impedance evaluation. The linear regression indicates a bias of 0.02 MRayls and an overall relative error of 1.7 % between the methods. This experimental setup holds promise for accurately assessing submerged structures, offering potential advantages for quantitative inspection. [ABSTRACT FROM AUTHOR]

In recent years, Taiwan’s government has focused on policies regarding offshore wind farming near the Indo-Pacific humpback dolphin habitat, where marine mammal observation is a critical consideration. The present research developed an algorithm called National Taiwan University Passive Acoustic Monitoring (NTU_PAM) to assist marine mammal observers (MMOs). The algorithm performs whistle detection processing and whistle localization. Whistle detection processing is based on image processing and whistle feature extraction; whistle localization is based on the time difference of arrival (TDOA) method. To test the whistle detection performance, we used the same data to compare NTU_PAM and the widely used software PAMGuard. To test whistle localization, we designed a real field experiment where a sound source projected simulated whistles, which were then recorded by several hydrophone stations. The data were analyzed to locate the moving path of the source. The results show that localization accuracy was higher when the sound source position was in the detection region composed of hydrophone stations. This paper provides a method for MMOs to conveniently observe the migration path and population dynamics of cetaceans without ecological disturbance.

In this work, the spatial correlation of a multipleinput multiple-output (MIMO) for underwater acoustic (UWA) channel is modeled. To obtain the spatial correlation for such a channel, a mathematical method to model the effect of the surface on the acoustic propagation is studied. The sea surface has a significant impact on the underwater acoustic propagation (UWA) channel since the sound field is scattered, particularly in rough sea conditions. In a situation where the sea surface is calm, the reflection is specular. In contrast, a sea surface subject to high sea states generates scattered waves. In these conditions, more complex mathematical equations are required to model the propagation. Current analytical models have limitations in terms of complexity and are not practical. Therefore, this study initially aims to consider a specular reflection to model the time-varying sea surface on the UWA channel. It is a simple model with low computationally complexity and can be used to assess the performance of UWA communications. Specifically, the specular reflection and transmission of an acoustic wave at a calm sea surface are studied, using the Huygens-Fresnel principle and the superposition theorem. The analytical model is developed using physical oceanic parameters representing the sea conditions. The results show a good agreement with the experimental analysis. Then, we aim to find a solution to analyze and model the surface scattering generated by the sea surface as a randomly rough surface generated by wind. The power spectrum as the Fourier transform of the correlation function of the wave height follows the Pierson and Moskowitz power spectrum which has a wide range of roughness scales. The results show a good agreement with the experimental analysis. [ABSTRACT FROM AUTHOR]

To increase underwater acoustic signal detectability and conserve energy, nodes leverage directional transmissions. In addition, nodes operate in a three-dimensional (3D) environment that is categorized as inhomogeneous where a propagating signal changes its direction based on the observed sound speed profile (SSP). Coupling 3D directional transmission with frequent node drifts and the varying underwater SSP complicates the process of selecting suitable transmission angles to maintain underwater communication links. Fundamentally, utilizing directional transmission while nodes are drifting causes breaks in established communication links and thus nodes need to find new angles to reestablish these links. Moreover, selecting arbitrary transmission angles may lead to overlapping beams or result in leaving an underwater region uncovered. To tackle the abovementioned challenges, this paper proposes an autonomous beam selection approach that optimizes underwater communication by selecting non-overlapping beams while mitigating the possibility of missing a region, i.e., maximize coverage. Such optimization is achieved by utilizing a structured angle selection mechanism that accounts for the capability of the used transducer. Moreover, we introduce an algorithm suited for resource constrained nodes to classify rays into different types. Then we divide the underwater medium into regions where each region is identified by the limits of the coverage area of each ray type. Finally, we utilize the limits of these regions to aid nodes in selecting the best ray to reestablish communication with drifted nodes. We validate our contribution through simulation where actual SSPs are leveraged to validate the beam classification process. [ABSTRACT FROM AUTHOR]

The locally resonant sonic material (LRSM) is an artificial metamaterial that can block underwater sound. The low-frequency insulation performance of LRSM can be enhanced by coupling local resonance and Bragg scattering effects. However, such method is hard to be experimentally proven as the best optimizing method. Hence, this paper proposes a statistical optimization method, which first finds a group of optimal solutions of an object function by utilizing genetic algorithm multiple times, and then analyzes the distribution of the fitness and the Euclidean distance of the obtained solutions, in order to verify whether the result is the global optimum. By using this method, we obtain the global optimal solution of the low-frequency insulation of LRSM. By varying parameters of the optimum, it can be found that the optimized insulation performance of the LRSM is contributed by the coupling of local resonance with Bragg scattering effect, as well as a distinct impedance mismatch between the matrix of LRSM and the surrounding water. This indicates coupling different effects with impedance mismatches is the best method to enhance the low-frequency insulation performance of LRSM. [ABSTRACT FROM AUTHOR]

Aiming at improving the accuracy of pulse ranging measurement in underwater multi-path environment, this paper proposes a novel pulse ranging algorithm based on active virtual time reversal (AVTR). By using the focusing characteristics of AVTR, the received signal can be focused at the receiving end, which eliminates the negative influence of multiple pseudo-peaks. In order to extract the received signal, we propose an energy-based adaptive windowed method which preserves the signal focus peak while truncates the side peak component. Numerical simulations are provided and outfield experiments are conducted. The results demonstrate the effectiveness of the proposed method comparing with correlation-based method.

Deep learning based methods have achieved state-of-the-art results on the task of ship type classification. However, most existing ship type classification algorithms take time–frequency (TF) features as input, the underlying discriminative information of these features has not been explored thoroughly. This paper proposes a novel feature optimization method which is designed to minimize an objective function aimed at increasing inter-class and reducing intra-class feature distance for ship type classification. The objective function we design is able to learn a center for each class and make samples from the same class closer to the corresponding center. This ensures that the features maximize underlying discriminative information involved in the data, particularly for some targets that usually confused by the conventional manual designed feature. Results on the dataset from a real environment show that the proposed feature optimization approach outperforms traditional TF features.

This article focuses on an underwater acoustic target recognition method based on target radiated noise. The difficulty of underwater acoustic target recognition is mainly the extraction of effective classification features and pattern classification. Traditional feature extraction methods based on Low Frequency Analysis Recording (LOFAR), Mel-Frequency Cepstral Coefficients (MFCC), Gammatone-Frequency Cepstral Coefficients (GFCC), etc. essentially compress data according to a certain pre-set model, artificially discarding part of the information in the data, and often losing information helpful for classification. This paper presents a target recognition method based on feature auto-encoding. This method takes the normalized frequency spectrum of the signal as input, uses a restricted Boltzmann machine to perform unsupervised automatic encoding of the data, extracts the deep data structure layer by layer, and classifies the acquired features through the BP neural network. This method was tested using actual ship radiated noise database, and the results show that proposed classification system has better recognition accuracy and adaptability than the hand-crafted feature extraction based method.

In underwater acoustic orthogonal frequency division multiplexing (OFDM) communication system, channel variations would introduce inter-carrier interference (ICI). This gets more severe as mobile speed, carrier frequency or OFDM symbol duration increases. In this paper, we propose a kind of parallel iterative ICI cancellation algorithm to combat that problem. The proposed method extracts channel variations from the adjacent OFDM symbols, and constantly improves the estimation accuracy of ICI through iteration. Finally, simulation and trial in real lake channel verify the availability and stability of the algorithm in the underwater acoustic time varying channel. In addition, this algorithm avoids the matrix inversion, reducing the complexity of the algorithm, and has the obvious advantage in accuracy and speed. [ABSTRACT FROM AUTHOR]