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1
Brown, David A., Paul J. Gendron, and John R. Buck. "Graduate education in acoustic engineering, transduction, and signal processing University of Massachusetts Dartmouth." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A123. http://dx.doi.org/10.1121/10.0015756.
Повний текст джерелаАнотація:
The University of Massachusetts Dartmouth has an established graduate program of study with a concentration in Applied Acoustics leading to the M.S. and Ph.D. degree in Electrical Engineering. The program offers courses and research opportunities in the area of electroacoustic transduction, underwater acoustics, and signal processing. Courses include the Fundamentals of Acoustics, Random Signals, Underwater Acoustics, Introduction to Transducers, Electroacoustic Transduction, Medical Ultrasonics, Digital Signal Processing, Detection Theory, and Estimation Theory. The ECE department established the university’s indoor underwater acoustic test and calibration facility which is one of the largest academic facilities supporting undergraduate and graduate thesis and sponsored research. The department has collaborations with many marine acoustic related companies including nearby Naval Undersea Warfare Center in Newport, RI and Woods Hole Oceanographic Institute in Cape Cod, MA. The presentation will highlight recent theses and dissertations, course offerings, and industry and government collaborations that support acoustical engineering, transduction, and signal processing.
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Yu, Miao, Yutong He, and Qian Kong. "Research on Pattern Extraction Method of Underwater Acoustic Signal Based on Linear Array." Mathematical Problems in Engineering 2022 (April 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/1819423.
Повний текст джерелаАнотація:
Underwater acoustic signal is an important reference data for marine information research. The research and application of underwater acoustic signal have been widely concerned and valued by countries and enterprises. With the needs of modern military development and the development of marine industry, the research and application of underwater acoustic signal will develop faster and faster. In order to better understand marine information, it is necessary to collect seawater acoustic signal data. Aiming at the purpose of recording underwater acoustic signals placed in the ocean for a long time, this study innovates the calibration and recording of large dynamic range of long-time underwater acoustic signals, improves the circuit setting methods such as receiving, amplification, and sampling, designs a large dynamic series of long-time underwater acoustic signal recording device, and adopts the linear array extraction method, so that it can monitor the underwater acoustic biological sound under the condition of low power. It can also monitor the blasting sound of offshore engineering. Hardware circuit design mainly includes main control chip selection, amplification circuit design, filter circuit design, analog-to-digital conversion circuit design, storage circuit design, and some auxiliary circuit design. The fourth chapter introduces the software development process of large dynamic range underwater acoustic signal recorder and mainly introduces the system development tool, system clock working method, real-time clock module working method, underwater acoustic signal acquisition method, data storage scheme design, and the use of FatFs file system. The underwater acoustic signal data is stored on a MicroSD in the form of TXT file; linear array extraction method is used for feature extraction. Compared to other methods, the transformer will suppress DC and low-frequency interference signals, thus achieving high-pass filtering characteristics. Finally, the performance and experimental results of the whole underwater acoustic signal recording device are analyzed. After testing, the underwater acoustic signal recording device designed in this paper works stably and can record underwater acoustic signals with large dynamic range for a long time in low-power mode.
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Gaudette, Jason E., and James A. Simmons. "Linear time-invariant (LTI) modeling for aerial and underwater acoustics." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A95. http://dx.doi.org/10.1121/10.0018285.
Повний текст джерелаАнотація:
Most newcomers to acoustic signal processing understand that linear time-invariant (LTI) filters can remove out-of-band noise from time series signals. What many acoustics researchers may not realize is that LTI models can be applied much more broadly, including to non-linear and time-variant systems. This presentation covers an overview of the autoregressive (AR), moving-average (MA), and autoregressive moving-average (ARMA) family of LTI models and their many useful applications in acoustics. Examples include analytic time-frequency processing of multi-component echolocation signals, fractional-delay filtering for acoustic time series simulations, broadband acoustic array beamforming, adaptive filtering for noise cancelation, and system identification for acoustic equalizers (i.e., flattening the frequency response of a source-receiver pair). This talk serves as a brief tutorial and inspiration for researchers who want to expand their use of signal processing, especially those in the fields of animal bioacoustics, aerial acoustics, and underwater acoustics.
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Taroudakis, Michael, Costas Smaragdakis, and N. Ross Chapman. "Denoising Underwater Acoustic Signals for Applications in Acoustical Oceanography." Journal of Computational Acoustics 25, no. 02 (January 25, 2017): 1750015. http://dx.doi.org/10.1142/s0218396x17500151.
Повний текст джерелаАнотація:
A method for denoising underwater acoustic signals used in applications of acoustical oceanography is presented. The method has been introduced for imaging denoising and has been modified to be applied with acoustic signals. The method keeps the energy significant part of the raw signal and reduces the effects of noise by comparing overlapping signal windows and keeping components which resemble true signal energy. It is shown by means of characteristic experiments in connection with a statistical signal characterization scheme based on wavelet transform, that using the statistical features of the wavelet sub-band coefficients of the denoised signal, tomography or geoacoustic inversions lead to a reliable estimation of the parameters of a marine environment.
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Ju, Yang, Zhengxian Wei, Li Huangfu, and Feng Xiao. "A New Low SNR Underwater Acoustic Signal Classification Method Based on Intrinsic Modal Features Maintaining Dimensionality Reduction." Polish Maritime Research 27, no. 2 (June 1, 2020): 187–98. http://dx.doi.org/10.2478/pomr-2020-0040.
Повний текст джерелаАнотація:
AbstractThe classification of low signal-to-noise ratio (SNR) underwater acoustic signals in complex acoustic environments and increasingly small target radiation noise is a hot research topic.. This paper proposes a new method for signal processing—low SNR underwater acoustic signal classification method (LSUASC)—based on intrinsic modal features maintaining dimensionality reduction. Using the LSUASC method, the underwater acoustic signal was first transformed with the Hilbert-Huang Transform (HHT) and the intrinsic mode was extracted. the intrinsic mode was then transformed into a corresponding Mel-frequency cepstrum coefficient (MFCC) to form a multidimensional feature vector of the low SNR acoustic signal. Next, a semi-supervised fuzzy rough Laplacian Eigenmap (SSFRLE) method was proposed to perform manifold dimension reduction (local sparse and discrete features of underwater acoustic signals can be maintained in the dimension reduction process) and principal component analysis (PCA) was adopted in the process of dimension reduction to define the reduced dimension adaptively. Finally, Fuzzy C-Means (FCMs), which are able to classify data with weak features was adopted to cluster the signal features after dimensionality reduction. The experimental results presented here show that the LSUASC method is able to classify low SNR underwater acoustic signals with high accuracy.
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Yan, Huichao, and Linmei Zhang. "Denoising of MEMS Vector Hydrophone Signal Based on Empirical Model Wavelet Method." Proceedings 15, no. 1 (July 8, 2019): 11. http://dx.doi.org/10.3390/proceedings2019015011.
Повний текст джерелаАнотація:
Underwater acoustic technology is a major method in current ocean research and exploration, which support the detection of seabed environment and marine life. However, the detection accuracy is directly affected by the quality of underwater acoustic signals collected by hydrophones. Hydrophones are efficient and important tools for collecting underwater acoustic signals. The collected signals of hydrophone often contain lots kinds of noise as the work environment is unknown and complex. Traditional signal denoising methods, such as wavelet analysis and empirical mode decomposition, product unsatisfied results of denoising. In this paper, a denoising method combining wavelet threshold processing and empirical mode decomposition is proposed, and correlation analysis is added in the signal reconstruction process. Finally, the experiment proves that the proposed denoising method has a better denoising performance. With the employment of the proposed method, the underwater acoustic signals turn smoothly and the signal drift of the collected hydroacoustic signal is improved. Comparing the signal spectrums of other methods, the spectral energy of the proposed denoising method is more concentrated, and almost no energy attenuation occurred.
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Li, Yuxing, Xiao Chen, Jing Yu, and Xiaohui Yang. "A Fusion Frequency Feature Extraction Method for Underwater Acoustic Signal Based on Variational Mode Decomposition, Duffing Chaotic Oscillator and a Kind of Permutation Entropy." Electronics 8, no. 1 (January 5, 2019): 61. http://dx.doi.org/10.3390/electronics8010061.
Повний текст джерелаАнотація:
In order to effectively extract the frequency characteristics of an underwater acoustic signal under sensor measurement, a fusion frequency feature extraction method for an underwater acoustic signal is presented based on variational mode decomposition (VMD), duffing chaotic oscillator (DCO) and a kind of permutation entropy (PE). Firstly, VMD decomposes the complex multi-component underwater acoustic signal into a set of intrinsic mode functions (IMFs), so as to extract the estimated center frequency of each IMF. Secondly, the frequency of the line spectrum can be obtained by using DCO and a kind of PE (KPE). DCO is used to detect the actual frequency of the line spectrum for each IMF and KPE can determine the accurate frequency when the phase space track is in the great periodic state. Finally, the frequency characteristic parameters acted as the input of the support vector machine (SVM) to distinguish different types of underwater acoustic signals. By comparing with the other three traditional methods for simulation signal and different kinds of underwater acoustic signals, the results show that the proposed method can accurately extract the frequency characteristics and effectively realize the classification and recognition for the underwater acoustic signal.
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Li, Yuxing, Yaan Li, Xiao Chen, Jing Yu, Hong Yang, and Long Wang. "A New Underwater Acoustic Signal Denoising Technique Based on CEEMDAN, Mutual Information, Permutation Entropy, and Wavelet Threshold Denoising." Entropy 20, no. 8 (July 28, 2018): 563. http://dx.doi.org/10.3390/e20080563.
Повний текст джерелаАнотація:
Owing to the complexity of the ocean background noise, underwater acoustic signal denoising is one of the hotspot problems in the field of underwater acoustic signal processing. In this paper, we propose a new technique for underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), mutual information (MI), permutation entropy (PE), and wavelet threshold denoising. CEEMDAN is an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD). First, CEEMDAN is employed to decompose noisy signals into many intrinsic mode functions (IMFs). IMFs can be divided into three parts: noise IMFs, noise-dominant IMFs, and real IMFs. Then, the noise IMFs can be identified on the basis of MIs of adjacent IMFs; the other two parts of IMFs can be distinguished based on the values of PE. Finally, noise IMFs were removed, and wavelet threshold denoising is applied to noise-dominant IMFs; we can obtain the final denoised signal by combining real IMFs and denoised noise-dominant IMFs. Simulation experiments were conducted by using simulated data, chaotic signals, and real underwater acoustic signals; the proposed denoising technique performs better than other existing denoising techniques, which is beneficial to the feature extraction of underwater acoustic signal.
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Yang, Shuang, and Xiangyang Zeng. "Combination of gated recurrent unit and Network in Network for underwater acoustic target recognition." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 6 (August 1, 2021): 486–92. http://dx.doi.org/10.3397/in-2021-1490.
Повний текст джерелаАнотація:
Underwater acoustic target recognition is an important part of underwater acoustic signal processing and an important technical support for underwater acoustic information acquisition and underwater acoustic information confrontation. Taking into account that the gated recurrent unit (GRU) has an internal feedback mechanism that can reflect the temporal correlation of underwater acoustic target features, a model with gated recurrent unit and Network in Network (NIN) is proposed to recognize underwater acoustic targets in this paper. The proposed model introduces NIN to compress the hidden states of GRU while retaining the original timing characteristics of underwater acoustic target features. The higher recognition rate and faster calculation speed of the proposed model are demonstrated with experiments for raw underwater acoustic signals comparing with the multi-layer stacked GRU model.
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Zhang, Zengmeng, Xing Cheng, Dayong Ning, Jiaoyi Hou, and Yongjun Gong. "Underwater acoustic beacon signal extraction based on dislocation superimposed method." Advances in Mechanical Engineering 9, no. 2 (February 2017): 168781401769167. http://dx.doi.org/10.1177/1687814017691671.
Повний текст джерелаАнотація:
Flight data are recorded in an acoustic beacon. A new signal extraction method led by random decrement technique is proposed to detect sound signals from thousands of meters under the sea. This method involves dislocation superimposed method and cross-correlation function to extract acoustic beacon signals with noise interference. First, the starting point is selected and the length of each segment is determined via two superposition ways. Second, the signal segment for linear superposition is intercepted to complete acoustic beacon signal extraction. Finally, the signals are subjected to cross-correlation and energy analyses to determine the accuracy of interception signals. During the experiment, the collected acoustic beacon signal is used as the test signal, and the signal is obtained as the simulation signal on the basis of the parameters of acoustic beacons. Results show that the correlation between the synthetic and concerned signals is more than 80% after a number of superposition are performed and the extraction effect is remarkable. Dislocation superimposed method is simple and easily operated, and the extracted signal waveform yields a high accuracy.
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Yao, Xiaohui, Honghui Yang, and Meiping Sheng. "Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks." Entropy 25, no. 2 (February 9, 2023): 318. http://dx.doi.org/10.3390/e25020318.
Повний текст джерелаАнотація:
Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method.
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Park, Hwijin, Yeong Bae Won, Sehyeong Jeong, Joo Young Pyun, Kwan Kyu Park, Jeong-Min Lee, Hee-Seon Seo, and Hak Yi. "Reflected Wave Reduction Based on Time-Delay Separation for the Plane Array of Multilayer Acoustic Absorbers." Sensors 21, no. 24 (December 17, 2021): 8432. http://dx.doi.org/10.3390/s21248432.
Повний текст джерелаАнотація:
This paper presents a control technique for reducing the reflection of acoustic signals for the plane array of multilayer acoustic absorbers underwater. In order to achieve this, a plane array of multilayer acoustic absorbers is proposed to attenuate low-frequency noise, with each unit consisting of a piezoelectric transducer, two layers of polyvinylidene fluorides and three layers of the acoustic window. Time-delay separation is used to find the incident and reflected acoustic signals to achieve reflected sound reduction. Experimental comparison of the attenuation rate of the reflected acoustic signal when performing passive and active controls is considered to verify the effectiveness of the time-delay separation technique applied plane array absorbers. Experiments on the plane array of smart skin absorbers confirmed that the reduction of reflected acoustic signals makes it suitable for a wide range of underwater applications.
13
Chen, Jie, Chang Liu, Jiawu Xie, Jie An, and Nan Huang. "Time–Frequency Mask-Aware Bidirectional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation." Sensors 22, no. 15 (July 26, 2022): 5598. http://dx.doi.org/10.3390/s22155598.
Повний текст джерелаАнотація:
Underwater acoustic signal separation is a key technique for underwater communications. The existing methods are mostly model-based, and cannot accurately characterize the practical underwater acoustic communication environment. They are only suitable for binary signal separation and cannot handle multivariate signal separation. However, recurrent neural networks (RNNs) show a powerful ability to extract the features of temporal sequences. Inspired by this, in this paper, we present a data-driven approach for underwater acoustic signal separation using deep learning technology. We use a bidirectional long short-term memory (Bi-LSTM) approach to explore the features of a time–frequency (T-F) mask, and propose a T-F-mask-aware Bi-LSTM for signal separation. Taking advantage of the sparseness of the T-F image, the designed Bi-LSTM network is able to extract the discriminative features for separation, which further improves the separation performance. In particular, this method breaks through the limitations of the existing methods and not only achieves good results in multivariate separation but also effectively separates signals when they are mixed with 40 dB Gaussian noise signals. The experimental results show that this method can achieve a 97% guarantee ratio (PSR), and the average similarity coefficient of the multivariate signal separation is stable above 0.8 under high noise conditions. It should be noted that our model can only handle known signals such as test signals for calibration.
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Li, Guohui, Qianru Guan, and Hong Yang. "Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising." Entropy 21, no. 1 (December 24, 2018): 11. http://dx.doi.org/10.3390/e21010011.
Повний текст джерелаАнотація:
Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), effort-to-compress complexity (ETC), refined composite multiscale dispersion entropy (RCMDE) and wavelet threshold denoising is proposed. Firstly, the original signal is decomposed into several IMFs by CEEMDAN and noise IMFs can be identified according to the ETC of IMFs. Then, calculating the RCMDE of remaining IMFs, these IMFs are divided into three kinds of IMFs by RCMDE, namely noise-dominant IMFs, real signal-dominant IMFs, real IMFs. Finally, noise IMFs are removed, wavelet soft threshold denoising is applied to noise-dominant IMFs and real signal-dominant IMFs. The denoised signal can be obtained by combining the real IMFs with the denoised IMFs after wavelet soft threshold denoising. Chaotic signals with different signal-to-noise ratio (SNR) are used for denoising experiments by comparing with EMD_MSE_WSTD and EEMD_DE_WSTD, it shows that the proposed algorithm has higher SNR and smaller root mean square error (RMSE). In order to further verify the effectiveness of the proposed method, which is applied to noise reduction of real underwater acoustic signals. The results show that the denoised underwater acoustic signals not only eliminate noise interference also restore the topological structure of the chaotic attractors more clearly, which lays a foundation for the further processing of underwater acoustic signals.
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Yu, Yang, Jie Shi, Ke He, and Peng Han. "The Control Packet Collision Avoidance Algorithm for the Underwater Multichannel MAC Protocols via Time-Frequency Masking." Discrete Dynamics in Nature and Society 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2437615.
Повний текст джерелаАнотація:
Establishing high-speed and reliable underwater acoustic networks among multiunmanned underwater vehicles (UUVs) is basic to realize cooperative and intelligent control among different UUVs. Nevertheless, different from terrestrial network, the propagation speed of the underwater acoustic network is 1500 m/s, which makes the design of the underwater acoustic network MAC protocols a big challenge. In accordance with multichannel MAC protocols, data packets and control packets are transferred through different channels, which lowers the adverse effect of acoustic network and gradually becomes the popular issues of underwater acoustic networks MAC protocol research. In this paper, we proposed a control packet collision avoidance algorithm utilizing time-frequency masking to deal with the control packets collision in the control channel. This algorithm is based on the scarcity of the noncoherent underwater acoustic communication signals, which regards collision avoiding as separation of the mixtures of communication signals from different nodes. We first measure the W-Disjoint Orthogonality of the MFSK signals and the simulation result demonstrates that there exists time-frequency mask which can separate the source signals from the mixture of the communication signals. Then we present a pairwise hydrophones separation system based on deep networks and the location information of the nodes. Consequently, the time-frequency mask can be estimated.
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Kim, Yong Guk, Dong Gwan Kim, Kyucheol Kim, Chang-Ho Choi, Nam In Park, and Hong Kook Kim. "An Efficient Compression Method of Underwater Acoustic Sensor Signals for Underwater Surveillance." Sensors 22, no. 9 (April 29, 2022): 3415. http://dx.doi.org/10.3390/s22093415.
Повний текст джерелаАнотація:
In this paper, we propose a new compression method using underwater acoustic sensor signals for underwater surveillance. Generally, sonar applications that are used for surveillance or ocean monitoring are composed of many underwater acoustic sensors to detect significant sources of sound. It is necessary to apply compression methods to the acquired sensor signals due to data processing and storage resource limitations. In addition, depending on the purposes of the operation and the characteristics of the operating environment, it may also be necessary to apply compression methods of low complexity. Accordingly, in this research, a low-complexity and nearly lossless compression method for underwater acoustic sensor signals is proposed. In the design of the proposed method, we adopt the concepts of quadrature mirror filter (QMF)-based sub-band splitting and linear predictive coding, and we attempt to analyze an entropy coding technique suitable for underwater sensor signals. The experiments show that the proposed method achieves better performance in terms of compression ratio and processing time than popular or standardized lossless compression techniques. It is also shown that the compression ratio of the proposed method is almost the same as that of SHORTEN with a 10-bit maximum mode, and both methods achieve a similar peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index on average.
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Xu, Kele, Qisheng Xu, Kang You, Boqing Zhu, Ming Feng, Dawei Feng, and Bo Liu. "Self-supervised learning–based underwater acoustical signal classification via mask modeling." Journal of the Acoustical Society of America 154, no. 1 (July 1, 2023): 5–15. http://dx.doi.org/10.1121/10.0019937.
Повний текст джерелаАнотація:
The classification of underwater acoustic signals has garnered a great deal of attention in recent years due to its potential applications in military and civilian contexts. While deep neural networks have emerged as the preferred method for this task, the representation of the signals plays a crucial role in determining the performance of the classification. However, the representation of underwater acoustic signals remains an under-explored area. In addition, the annotation of large-scale datasets for the training of deep networks is a challenging and expensive task. To tackle these challenges, we propose a novel self-supervised representation learning method for underwater acoustic signal classification. Our approach consists of two stages: a pretext learning stage using unlabeled data and a downstream fine-tuning stage using a small amount of labeled data. The pretext learning stage involves randomly masking the log Mel spectrogram and reconstructing the masked part using the Swin Transformer architecture. This allows us to learn a general representation of the acoustic signal. Our method achieves a classification accuracy of 80.22% on the DeepShip dataset, outperforming or matching previous competitive methods. Furthermore, our classification method demonstrates good performance in low signal-to-noise ratio or few-shot settings.
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Jiang, Cheng, JianLong Li, and Wen Xu. "The Use of Underwater Gliders as Acoustic Sensing Platforms." Applied Sciences 9, no. 22 (November 12, 2019): 4839. http://dx.doi.org/10.3390/app9224839.
Повний текст джерелаАнотація:
Underwater gliders travel through the ocean by buoyancy control, which makes their motion silent and involves low energy consumption. Due to those advantages, numerous studies on underwater acoustics have been carried out using gliders and different acoustic payloads have been developed. This paper aims to illustrate the use of gliders in underwater acoustic observation and target detection through experimental data from two sea trials. Firstly, the self-noise of the glider is analyzed to illustrate its feasibility as an underwater acoustic sensing platform. Then, the ambient noises collected by the glider from different depths are presented. By estimating the transmission loss, the signal receiving ability of the glider is assessed, and a simulation of target detection probability is performed to show the advantages of the glider over other underwater vehicles. Moreover, an adaptive line enhancement is presented to further reduce the influence of self-noise. Meanwhile, two hydrophones are mounted at both ends of the glider to form a simple array with a large aperture and low energy consumption. Thus, the target azimuth estimation is verified using broadband signals, and a simple scheme to distinguish the true angle from the port‒starboard ambiguity is presented. The results indicate that the glider does have advantages in long-term and large-scale underwater passive sensing.
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Ma, Fuyin, Linbo Wang, Pengyu Du, Chang Wang, and Jiu Hui Wu. "A three-dimensional broadband underwater acoustic concentrator." Journal of Physics D: Applied Physics 55, no. 19 (February 16, 2022): 195110. http://dx.doi.org/10.1088/1361-6463/ac4720.
Повний текст джерелаАнотація:
Abstract We propose a three-dimensional (3D) omnidirectional underwater acoustic concentrator based on the concept of an acoustic prison, which can realize a substantial enhancement of underwater sound signals in broadband ranges. This device mainly employs the non-resonant multiple reflection characteristics of the semi-enclosed geometric space, so it has a wide working frequency bandwidth. Compared with previously reported concentrators based on transform acoustic mechanisms, the structure is more simple and, most importantly, it can realize omnidirectional signal enhancement in a 3D space. Moreover, the working frequency band of this acoustic concentrator depends on the size of the concentrator, so it can be changed directly through size scaling, which is convenient for engineering applications. In general, the designed underwater acoustic concentrator has the advantages of a simple structure, scalability, large bandwidth of working frequency, and high signal gain. It has potential applications in underwater target detection and other aspects.
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Grelowska, Grażyna, and Eugeniusz Kozaczka. "Underwater Acoustic Imaging of the Sea." Archives of Acoustics 39, no. 4 (March 1, 2015): 439–52. http://dx.doi.org/10.2478/aoa-2014-0048.
Повний текст джерелаАнотація:
Abstract Acoustic waves are a carrier of information mainly in environments where the use of other types of waves, for example electromagnetic waves, is limited. The term acoustical imaging is widely used in the ultrasonic engineering to imaging areas in which the acoustic waves propagate. In particular, ultrasound is widely used in the visualization of human organs-ultrasonography (Nowicki, 2010). Expanding the concept, acoustical imaging can also be used to presentation (monitoring) the current state of sound intensity distribution leading to characterization of sources in observed underwater region. This can be represented in the form of an acoustic characteristic of the area, for example as a spectrogram. Knowledge of the underwater world which is built by analogy to the perception of the space on the Earth's surface is to be systematize in the form of images. Those images arise as a result of graphical representation of processed acoustic signals. In this paper, it is explained why acoustic waves are used in underwater imaging. Furthermore, the passive and active systems for underwater observation are presented. The paper is illustrated by acoustic images, most of them originated from our own investigation.
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Yang, Hong, Lipeng Gao, and Guohui Li. "Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine." Complexity 2020 (April 24, 2020): 1–17. http://dx.doi.org/10.1155/2020/6947059.
Повний текст джерелаАнотація:
Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series.
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Bhardwaj, Ananya, Nizar Somaan, Tillson Galloway, and Karim G. Sabra. "Improving passive acoustic target detection using machine learning classifiers." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A346. http://dx.doi.org/10.1121/10.0019104.
Повний текст джерелаАнотація:
The ocean covers 70% of the Earth surface area, yet over 80% of it remains unexplored despite the advances in underwater acoustics and oceanography. Ocean exploration is critical for accurate climate model development, renewable energy applications, and in understanding the marine habitat. Further exploration necessitates improvements in underwater navigation with Autonomous Underwater Vehicles (AUVs). Utilizing acoustic landmarks can enhance AUV localization performance [Fula et al., Oceans IEEE/MTS (2018)]. Passive markers called Acoustic Identification (AID) Tags have unique and identifiable acoustic reflection signatures designed to function as landmarks [Satish et al., JASA 149 (2021)]. These targets can be detected by Match Filtering returns with template signals. Match Filtering performance is limited in the presence of strong interfering signals, and with changes in sound speeds that alters the temporal structure of these signatures. The application of a Machine Learning classifiers for detecting AID tag signatures can improve the localization performance. Through implementation of Logistic Regression, Deep Neural Networks, and Convolutional Neural Networks, the generalizability and superiority of these models is demonstrated. We report high accuracies (>90 %) in a multilabel (8 classes) classification task with signals with low SNR (–6 dB) and strong interference (+12 dB).
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Frasier, Kaitlin E. "A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets." PLOS Computational Biology 17, no. 12 (December 3, 2021): e1009613. http://dx.doi.org/10.1371/journal.pcbi.1009613.
Повний текст джерелаАнотація:
Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and progress has been limited by challenges related to the lack of labeled datasets adequate for training and testing. Large quantities of known and as yet unidentified broadband signal types mingle in marine recordings, with variability introduced by acoustic propagation, source depths and orientations, and interacting signals. Manual classification of these datasets is unmanageable without an in-depth knowledge of the acoustic context of each recording location. A signal classification pipeline is presented which combines unsupervised and supervised learning phases with opportunities for expert oversight to label signals of interest. The method is illustrated with a case study using unsupervised clustering to identify five toothed whale echolocation click types and two anthropogenic signal categories. These categories are used to train a deep network to classify detected signals in either averaged time bins or as individual detections, in two independent datasets. Bin-level classification achieved higher overall precision (>99%) than click-level classification. However, click-level classification had the advantage of providing a label for every signal, and achieved higher overall recall, with overall precision from 92 to 94%. The results suggest that unsupervised learning is a viable solution for efficiently generating the large, representative training sets needed for applications of deep learning in passive acoustics.
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Hu, Gang, Kejun Wang, Yuan Peng, Mengran Qiu, Jianfei Shi, and Liangliang Liu. "Deep Learning Methods for Underwater Target Feature Extraction and Recognition." Computational Intelligence and Neuroscience 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1214301.
Повний текст джерелаАнотація:
The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.
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Di Bona, Isabella, Christopher Gravelle, Zakaria Faddi, David A. Brown, and Corey Bachand. "Underwater acoustic spiral wave navigation system." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A115. http://dx.doi.org/10.1121/10.0010826.
Повний текст джерелаАнотація:
A spiral wave navigation beacon is described that creates an outgoing signal with a wavefront that is a diverging spiral cylindrical wave followed by a reference signal of circular wavefront as previously described by Dzikowicz and Hefner in JASA. In this demonstration, the signals are created by driving a cylindrical transducer’s orthogonal sine and cosine modes of vibration in phase quadrature as previously described by Brown and Bachand in JASA. The two dipolar radiation patterns combine to create a helically diverging spiral wavefront that may be used for navigation and communication. The electronic system design and transducer characterization will be presented and are the subject a senior-capstone design project in the College of Engineering at UMass Dartmouth for four of the authors (I.D.B., C.G., K.C., Z.F.). [The project provided a multidisciplinary opportunity in acoustical engineering, instrumentation, calibration, and signal processing and was sponsored by industry (BTech).]
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Kalyu, V. A., D. A. Smirnov, V. I. Tarovik, M. S. Sergeev, and V. V. Petrova. "The environmental safety of the Russian arctic shelf waters and improving the safety of marine ecosystems by reducing the noise pollution." Transactions of the Krylov State Research Centre 2, no. 404 (June 6, 2023): 140–53. http://dx.doi.org/10.24937/2542-2324-2023-2-404-140-153.
Повний текст джерелаАнотація:
Object and purpose of research. Ensuring the environmental safety of the Russian shelf waters requires constant development of methods and technologies related to monitoring, quantitative assessment and reduction of the harmful effects of pollution sources. During the industrial and transport development of the water area of the Northern Sea Route (NSR), an intensive increase of underwater noise is observed. Obtaining an adequate picture of the impact of underwater noise sources on representatives of the marine ecosystem creates a need to involve more and more modern and accurate measurement techniques. The article describes the draft methods for measuring the self-noise of carrier vessel and underwater noise of a selected marine equipment object, which were developed in order to identify the degree of impact of noise pollution over to the ecological situation in the considered water area. Materials and methods. An hydro acoustic signals contains the self-noise of measuring vessel, the noise of an object of marine industrial equipment (OMIE) and the noise of the water area where acoustic tests are going on. This acoustic signals are perceived by combined sound pressure and sound pressure gradient transducers, as well as by an omnidirectional hydrophone, located in the receiving system , and are converted into electrical signals transmitted via the main cable to the onboard post of the measuring vessel. Signals are sent to the hydro acoustic guidance beacon via the same cable to control the operation of this beacon. The information processing procedure is set out in the SIGAK VP Operation Manual (MGFK.411711.327 RE). The main purpose of the primary processing is to obtain 1/3-octave spectra and levels of underwater noise generated by the tested carrier vessel or OMIE, as well as the noise of the water area where these tests are carried out. In accordance with the accepted classification, the method of measuring sound pressure levels using a hydro acoustic complex according to this technique refers to indirect methods with single observations. Main results. In the course of the work, preliminary methods for measuring the self-noise of measuring vessel and underwater noise of a selected marine industrial object were developed. To measure the levels of underwater noise in 1/3-octave frequency bands in the range from 5 Hz to 10,000 Hz, within the framework of the draft methods, it is planned to use a stationary measuring hydro acoustic complex with a vector receiver from the GIK-VP. The signal processing algorithms are based on the spatial-frequency filtering of the acoustic power flux density components, which makes it possible to protect the measurement information from interference signals whose propagation direction does not coincide with the direction to the measured object. Conclusion. The results obtained in the form of implemented preliminary methods are important for the creation of regulatory documentation for the regulation of technogenic underwater noise in the waters of the Russian jurisdiction, reducing the intensity of noise pollution and the detrimental impact over to marine ecosystems. The article targeting the support at the stages of acoustical marine technical design, construction and operation of offshore industrial facilities and ships of various types, acoustic monitoring of the compliance of marine equipment with international standards for underwater noise.
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Weiss, L. G., and T. L. Dixon. "Wavelet-based denoising of underwater acoustic signals." Journal of the Acoustical Society of America 101, no. 1 (January 1997): 377–83. http://dx.doi.org/10.1121/1.417983.
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Krieger, John R., and Georges L. Chahine. "Acoustic signals of underwater explosions near surfaces." Journal of the Acoustical Society of America 118, no. 5 (November 2005): 2961–74. http://dx.doi.org/10.1121/1.2047147.
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Wang, Maofa, Zhenjing Zhu, and Gaofeng Qian. "Modulation Signal Recognition of Underwater Acoustic Communication Based on Archimedes Optimization Algorithm and Random Forest." Sensors 23, no. 5 (March 2, 2023): 2764. http://dx.doi.org/10.3390/s23052764.
Повний текст джерелаАнотація:
This paper researches the recognition of modulation signals in underwater acoustic communication, which is the fundamental prerequisite for achieving noncooperative underwater communication. In order to improve the accuracy of signal modulation mode recognition and the recognition effects of traditional signal classifiers, the article proposes a classifier based on the Archimedes Optimization Algorithm (AOA) and Random Forest (RF). Seven different types of signals are selected as recognition targets, and 11 feature parameters are extracted from them. The decision tree and depth obtained by the AOA algorithm are calculated, and the optimized random forest after the AOA algorithm is used as the classifier to achieve the recognition of underwater acoustic communication signal modulation mode. Simulation experiments show that when the signal-to-noise ratio (SNR) is higher than −5dB, the recognition accuracy of the algorithm can reach 95%. The proposed method is compared with other classification and recognition methods, and the results show that the proposed method can ensure high recognition accuracy and stability.
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Zhang, Lan, Xiao Mei Xu, Wei Feng, and You Gan Chen. "Doppler Estimation, Synchronization with HFM Signals for Underwater Acoustic Communications." Applied Mechanics and Materials 198-199 (September 2012): 1638–45. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1638.
Повний текст джерелаАнотація:
This paper presents the application of the hyperbolic frequency modulated (HFM) signal to acoustic propagation in order to improve the performance of underwater acoustic (UWA) communications. Due to the large delay spread caused by multipath propagation and the severe Doppler Effect of the channel, we propose the using of double HFM signals as preambles for Doppler estimation, frame synchronization in UWA communications. A theoretical analysis about Doppler-invariability of HFM signals was provided firstly, then some numerical simulations about Doppler estimation were implemented, and experiments on testing performance of double-HFM preambles for frame synchronization in the pool were carried out as well. The simulation and experimental results show that using double-HFM signals as preambles has the capability to take an explicit Doppler estimation and retiming for frame synchronization, demonstrating that it has a good prospect in underwater acoustic communication system, especially for moving platforms.
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Somaan, Nizar, Ananya Bhardwaj, and Karim G. Sabra. "Passive underwater Acoustic IDentification (AID) tags for enhancing Autonomous Underwater Vehicle (AUV) navigation during docking or homing operations." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A345. http://dx.doi.org/10.1121/10.0019102.
Повний текст джерелаАнотація:
Autonomous underwater vehicle (AUV) navigation requires accurate positioning information from the surrounding environment, especially for tasks such as AUV homing or docking operations. Previous literature has introduced a class of low-cost passive underwater acoustic markers, termed Acoustic IDentification (AID) tags [Satish and Sabra, J. Acoust. Soc. Am. 149(5), 3387–3405 (2021)] built of multi-layer shells with different acoustic properties and thicknesses to generate a uniquely engineered acoustic signature, composed of the multiple reflections created by the layer interfaces. These passive AID tags can be detected by an AUV instrumented with a high-frequency sonar transducer at significantly greater distances than conventional optical methods, especially in turbid waters. Additionally using AID tags as navigation-aid can also alleviate the need of relying on active acoustic transponders. An implementation of a constellation of AID tags enabling fine underwater positioning an AUV towards a docking station or for homing purposes will be presented to provide proof of concept. Furthermore, the optimization of the design of the AID tags for this application aswell as specific signal processing detection methodologies to improve thedetectability of AID tags in the presence of interfering signals (e.g., clutter) will be discussed. [Work supported by the Office of Naval Research].
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Lin, Chin-Feng, Tsung-Jen Su, Hung-Kai Chang, Chun-Kang Lee, Shun-Hsyung Chang, Ivan A. Parinov, and Sergey Shevtsov. "Direct-Mapping-Based MIMO-FBMC Underwater Acoustic Communication Architecture for Multimedia Signals." Applied Sciences 10, no. 1 (December 27, 2019): 233. http://dx.doi.org/10.3390/app10010233.
Повний текст джерелаАнотація:
In this paper, a direct-mapping (DM)-based multi-input multi-output (MIMO) filter bank multi-carrier (FBMC) underwater acoustic multimedia communication architecture (UAMCA) is proposed. The proposed DM-based MIMO-FBMC UAMCA is rare and non-obvious in the underwater multimedia communication research topic. The following are integrated into the proposed UAMCA: A 2 × 2 DM transmission mechanism, a (2000, 1000) low-density parity-check code encoder, a power assignment mechanism, an object-composition petrinet mechanism, adaptive binary phase shift keying modulation and 4-offset quadrature amplitude modulation methods. The multimedia signals include voice, image, and data. The DM transmission mechanism in different spatial hardware devices transmits different multimedia packets. The proposed underwater multimedia transmission power allocation algorithm (UMTPAA) is simple, fast, and easy to implement, and the threshold transmission bit error rates (BERs) and real-time requirements for voice, image, and data signals can be achieved using the proposed UMTPAA. The BERs of the multimedia signals, data symbol error rates of the data signals, power saving ratios of the voice, image and data signals, mean square errors of the voice signals, and peak signal-to-noise ratios of the image signals, for the proposed UAMCA with a perfect channel estimation, and channel estimation errors of 5%, 10%, and 20%, respectively, were explored and demonstrated. Simulation results demonstrate that the proposed 2 × 2 DM-based MIMO-FBMC UAMCA is suitable for low power and high speed underwater multimedia sensor networks.
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Li, Tong Xu, Xiao Min Zhang, and Yu Chen. "Design and Implementation of a New Type of Underwater Acoustic Target Simulator." Applied Mechanics and Materials 397-400 (September 2013): 2200–2204. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2200.
Повний текст джерелаАнотація:
According to the actual needs of underwater acoustic homing system in system debugging and experiment parameters setting, a new design of real-time water target simulator is introduced. This system simulates the acoustic reflection characteristics of targets, receives active detection signal and emits signals of reflection echo characteristics. The target simulator can response up to two active detection signals in the condition of interference. This system is complete, accurate and can be extended. Users can monitor the working state of simulator and change the parameters of simulator. As a result of an echoic tank experiment and lake experiment, the system has been successfully applied to the underwater homing system.
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Allam, Ahmed, Waleed Akbar, and Fadel Adib. "An analytical framework for low-power underwater backscatter communications." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A376. http://dx.doi.org/10.1121/10.0019235.
Повний текст джерелаАнотація:
Acoustic underwater backscatter enables ultra-low-power communication with applications in ocean exploration, monitoring, navigation, and aquaculture. Unlike traditional communication systems, acoustic backscatter does not require active signal generation. Instead, it communicates data by modulating existing acoustic signals, requiring few microwatts of power for operation. Backscatter communication enables ultra-low power sensors by switching between absorbing and reflecting acoustic waves transmitted from a central station. The energy burden is shifted to the transmitting station, and the acoustic power supplied by the station can power the sensor, allowing for battery-free operation. Initial demonstrations of underwater backscatter communication were encouraging; however, the theoretical and practical limits are still unknown. In this work, we develop a multiphysics analytical framework for the communication and power link budget of underwater backscatter. The framework calculates practical communication and power-up range, transmitter power budget, and signal-to-noise ratio, accounting for transducers’ characteristics and the underwater communication channel. The analytical predictions are validated for practical transducers using high-fidelity piezo-acoustic finite element simulations and experimental measurements. The framework will guide future backscatter systems design, identifying practical operation ranges and optimal frequencies for data transmission and batteryless operation.
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MURUGAN, S. SAKTHIVEL, and V. NATARAJAN. "IMPLEMENTATION OF THRESHOLD DETECTION TECHNIQUE FOR EXTRACTION OF COMPOSITE SIGNALS AGAINST AMBIENT NOISES IN UNDERWATER COMMUNICATION USING EMPIRICAL MODE DECOMPOSITION." Fluctuation and Noise Letters 11, no. 04 (December 2012): 1250031. http://dx.doi.org/10.1142/s0219477512500319.
Повний текст джерелаАнотація:
Acoustic signals transmitted in underwater for distance communication are affected by numerous factors, random events, and corrupted with ambient noise, making them nonlinear and nonstationary in nature. Ambient noises are the background acoustic noises in the sea due to natural and manmade sources like wind, rain, seismic, marine species, harbor activities, motor on the boat, ship traffic, etc. In recent years, the application of Empirical Mode Decomposition (EMD) technique to analyze nonlinear and nonstationary signals has gained importance. In this paper an EMD system is proposed with an algorithm by implementing FFT to identify and extract all the acoustic stationary signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in shallow water region. Further a new threshold detection technique is also incorporated in the algorithm for detection and extraction of composite signals that are not extracted properly. The threshold is calculated using the mean and variance of the noisy signal generated by various ambient noises in the ocean. The algorithm is also validated by transmitting three reference acoustic signals. The proposed EMD approach with threshold detector algorithm identifies and extracts all the signals transmitted along with other stationary signals available in the ocean against various ambient noises.
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Liu, Tao, Jian Gan Wang, and Si Guang Zong. "Experimental Investigation on Underwater Opto-Acoustic Communication." Applied Mechanics and Materials 143-144 (December 2011): 653–57. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.653.
Повний текст джерелаАнотація:
The underwater opto-acoustic communication system for directional acoustic communication between an in-air platform and a submerged platform operating is important. The paper presents a new method to solve this problem with opto-acoustic technology, which combines high-energy laser, the opto-acoustic transmitter that optical energy is converted to acoustic energy at the water surface. The laser-based transmitter provides a versatile method for generating underwater sound. The acoustic pressure is linearly proportional to the laser power. The paper designed an experimental measurement system for the opto-acoustic communication. It made experiments for study on the waveform and spectrum characteristics of opto-acoustic signals. The paper also discuss the acoustic wave after optical breakdown in water with Nd:YAG laser pulses. The opto-acoustic signals can controll by adjusting the laser's parameters. The conclusion is that the opto-acoustic communication has some technical advantages. This system presents a change in the way communicational from the air.
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Zhou, Hanyun, S. H. Huang, and Wei Li. "Parametric Acoustic Array and Its Application in Underwater Acoustic Engineering." Sensors 20, no. 7 (April 10, 2020): 2148. http://dx.doi.org/10.3390/s20072148.
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As a sound transmitting device based on the nonlinear acoustic theory, parametric acoustic array (PAA) is able to generate high directivity and low frequency broadband signals with a small aperture transducer. Due to its predominant technical advantages, PAA has been widely used in a variety of application scenarios of underwater acoustic engineering, such as sub-bottom profile measurement, underwater acoustic communication, and detection of buried targets. In this review paper, we examine some of the important advances in the PAA since it was first proposed by Westervelt in 1963. These advances include theoretical modelling for the PAA, signal processing methods, design considerations and implementation issues, and applications of the PAA in underwater acoustic engineering. Moreover, we highlight some technical challenges which impede further development of the PAA, and correspondingly give a glimpse on its possible extension in the future. This article provides a comprehensive overview of some important works of the PAA and serves as a quick tutorial reference to readers who are interested to further explore and extend this technology, and bring this technology to other application areas.
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Li, Jiangqiao, Li Jiang, Fujian Yu, Ye Zhang, and Kun Gao. "Research on improving measurement accuracy of acoustic transfer function of underwater vehicle." MATEC Web of Conferences 336 (2021): 01006. http://dx.doi.org/10.1051/matecconf/202133601006.
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To address the problem that acoustic transfer functions with underwater platforms cannot be measured accurately, this paper presents a method based on phase compensation to improve the accuracy of acoustic transfer function measurements on underwater platforms. The time-domain impulse response signals with multiple cycles are first collected and intercepted, and then their phase differences are estimated using the least-squares method, and phase compensation is used to align the phases of all the signals, and then the impulse response signals are weighted and averaged over all the impulse response signals to cancel out the random noise. The water pool test proves that this method reduces the measurement random noise while obtaining a high-fidelity time domain transfer function, which effectively improves the signal-to-noise ratio of the measurement. The method adopts only one measurement signal, and without changing the measurement system, the random noise is cancelled out by the in-phase superposition of the multi-cycle impulse response signals to avoid the nonlinear distortion of the measurement results.
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Wang, Xingmei, Anhua Liu, Yu Zhang, and Fuzhao Xue. "Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network." Remote Sensing 11, no. 16 (August 13, 2019): 1888. http://dx.doi.org/10.3390/rs11161888.
Повний текст джерелаАнотація:
A method with a combination of multi-dimensional fusion features and a modified deep neural network (MFF-MDNN) is proposed to recognize underwater acoustic targets in this paper. Specifically, due to the complex and changeable underwater environment, it is difficult to describe underwater acoustic signals with a single feature. The Gammatone frequency cepstral coefficient (GFCC) and modified empirical mode decomposition (MEMD) are developed to extract multi-dimensional features in this paper. Moreover, to ensure the same time dimension, a dimension reduction method is proposed to obtain multi-dimensional fusion features in the original underwater acoustic signals. Then, to reduce redundant features and further improve recognition accuracy, the Gaussian mixture model (GMM) is used to modify the structure of a deep neural network (DNN). Finally, the proposed underwater acoustic target recognition method can obtain an accuracy of 94.3% under a maximum of 800 iterations when the dataset has underwater background noise with weak targets. Compared with other methods, the recognition results demonstrate that the proposed method has higher accuracy and strong adaptability.
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Oh, Raegeun, Taek Lyul Song, and Jee Woong Choi. "Batch Processing through Particle Swarm Optimization for Target Motion Analysis with Bottom Bounce Underwater Acoustic Signals." Sensors 20, no. 4 (February 24, 2020): 1234. http://dx.doi.org/10.3390/s20041234.
Повний текст джерелаАнотація:
A target angular information in 3-dimensional space consists of an elevation angle and azimuth angle. Acoustic signals propagating along multiple paths in underwater environments usually have different elevation angles. Target motion analysis (TMA) uses the underwater acoustic signals received by a passive horizontal line array to track an underwater target. The target angle measured by the horizontal line array is, in fact, a conical angle that indicates the direction of the signal arriving at the line array sonar system. Accordingly, bottom bounce paths produce inaccurate target locations if they are interpreted as azimuth angles in the horizontal plane, as is commonly assumed in existing TMA technologies. Therefore, it is necessary to consider the effect of the conical angle on bearings-only TMA (BO-TMA). In this paper, a target conical angle causing angular ambiguity will be simulated using a ray tracing method in an underwater environment. A BO-TMA method using particle swarm optimization (PSO) is proposed for batch processing to solve the angular ambiguity problem.
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Janapati, Yellaiah. "Laser-induced sonar: A promising approach for improved underwater acoustic sensing." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A68. http://dx.doi.org/10.1121/10.0022821.
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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.
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Campo-Valera, María, and Ivan Felis. "Underwater Acoustic Communication for The Marine Environment’s Monitoring." Proceedings 42, no. 1 (November 14, 2019): 51. http://dx.doi.org/10.3390/ecsa-6-06642.
Повний текст джерелаАнотація:
Within the possibilities of non-linear acoustics, the parametric effect offers a range of acoustic applications that are currently being exploited in different areas. In underwater acoustics, environmental monitoring and security are one of the applications that can benefit from these technologies, allowing the transmission of information in a directivity controlled and efficient manner. An essential aspect for the optimal functioning of these technologies is the choice of the modulation that best suits the needs of communication. In the present work, different modulation techniques are explained, through their non-linear propagation, that allows generating the signals to be propagated. Among the modulations presented in this work, we have Amplitude Modulation (AM), Continuous Phase Frequency Shift Keying (CPFSK), and Linear Frequency Modulation (LFM) modulations normally used in communications. These modulations are performed with a modulating signal (sine and sine-sweeps type) whose non-linear demodulation determines the shape of the 1 and 0 bits, through the transmission of a bit string. With all this, comparisons are made between each technique, to obtain a more precise detection and discrimination of the bits.
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Li, Guohui, Zhichao Yang, and Hong Yang. "Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient." Entropy 20, no. 12 (November 30, 2018): 918. http://dx.doi.org/10.3390/e20120918.
Повний текст джерелаАнотація:
Noise reduction of underwater acoustic signals is of great significance in the fields of military and ocean exploration. Based on the adaptive decomposition characteristic of uniform phase empirical mode decomposition (UPEMD), a noise reduction method for underwater acoustic signals is proposed, which combines amplitude-aware permutation entropy (AAPE) and Pearson correlation coefficient (PCC). UPEMD is a recently proposed improved empirical mode decomposition (EMD) algorithm that alleviates the mode splitting and residual noise effects of EMD. AAPE is a tool to quantify the information content of nonlinear time series. Unlike permutation entropy (PE), AAPE can reflect the amplitude information on time series. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by UPEMD. The AAPE of each IMF is calculated. The modes are separated into high-frequency IMFs and low-frequency IMFs, and all low-frequency IMFs are determined as useful IMFs (UIMFs). Then, the PCC between the high-frequency IMF with the smallest AAPE and the original signal is calculated. If PCC is greater than the threshold, the IMF is also determined as a UIMF. Finally, all UIMFs are reconstructed and the denoised signal is obtained. Chaotic signals with different signal-to-noise ratios (SNRs) are used for denoising experiments. Compared with EMD and extreme-point symmetric mode decomposition (ESMD), the proposed method has higher SNR and smaller root mean square error (RMSE). The proposed method is applied to noise reduction of real underwater acoustic signals. The results show that the method can further eliminate noise and the chaotic attractors are smoother and clearer.
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MURUGAN, S. SAKTHIVEL, V. NATARAJAN, and S. RADHA. "ANALYSIS OF MNLMS AND KLMS ALGORITHM FOR UNDERWATER ACOUSTIC COMMUNICATIONS." Fluctuation and Noise Letters 11, no. 04 (December 2012): 1250023. http://dx.doi.org/10.1142/s021947751250023x.
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The use of adaptive filters to alleviate the degradation caused by wind driven ambient noise in shallow water is considered in this paper. Since, underwater acoustic signals are greatly affected by the ocean interference and ambient noise disturbances when propagating through underwater channels, an effective adaptive filtering system is necessary for denoising the signal which are degraded by noise. Least mean square (LMS), normalized LMS (NLMS), Modified New LMS (MNLMS) and Kalman LMS (KLMS) based adaptive algorithms are analyzed in terms of their performance with the aid of performance measure characteristics such as signal to noise ratio (SNR) and mean square error (MSE). The MNLMS is developed by calculating an optimum learning parameter that best suits for the acoustic signal used. The analysis is carried out for a range of 100 Hz to 10 KHz source signals and the algorithm proves that any ambient noise signals against the source signal in this range can be eliminated and the source signal can be reconstructed. Our simulation results show that KLMS and MNLMS algorithms achieve remarkable performance even in the very low SNR region as compared to LMS and NMLS algorithms. Moreover, it is observed that the output convergence is also very fast for MNLMS and KLMS.
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Zhang, Run, Chengbing He, Lianyou Jing, Chaopeng Zhou, Chao Long, and Jiachao Li. "A Modulation Recognition System for Underwater Acoustic Communication Signals Based on Higher-Order Cumulants and Deep Learning." Journal of Marine Science and Engineering 11, no. 8 (August 21, 2023): 1632. http://dx.doi.org/10.3390/jmse11081632.
Повний текст джерелаАнотація:
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.
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Li, Yuanyuan, and Shucheng Liang. "Research on modulation recognition of underwater acoustic communication signal based on deep learning." Journal of Physics: Conference Series 2435, no. 1 (February 1, 2023): 012007. http://dx.doi.org/10.1088/1742-6596/2435/1/012007.
Повний текст джерелаАнотація:
Abstract Marine information technology plays a important role in the development of marine resources development, marine climate early warning, and other industries. Underwater acoustic communication technology can help us better access marine information. The performance of the underwater acoustic signal modulation recognition algorithm depends on the accuracy of feature extraction. however, due to excessive underwater noise, many traditional algorithms can not recognize features well. For this reason, this paper proposes a modulation recognition network for aquatic communication signals based on deep learning. ResNet can capture the characteristics of deep features and combines ResNet with a modulation recognition network. Finally, the experiment proves the effectiveness of this method.
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Liu, Cong, Dong Han, Xinyang Zhang, and Ning Li. "Research on Feature Extraction of Underwater Acoustic Target Radiation Noise Based on Machine Learning Algorithm." Journal of Physics: Conference Series 2644, no. 1 (November 1, 2023): 012008. http://dx.doi.org/10.1088/1742-6596/2644/1/012008.
Повний текст джерелаАнотація:
Abstract 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.
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Hu, Yalin, Jixin Bao, Wanzhong Sun, and Xiaomei Fu. "Modulation Recognition Method for Underwater Acoustic Communication Signals Based on Passive Time Reversal-Autoencoder with the Synchronous Signals." Sensors 23, no. 13 (June 28, 2023): 5997. http://dx.doi.org/10.3390/s23135997.
Повний текст джерелаАнотація:
In the process of the modulation recognition of underwater acoustic communication signals, the multipath effect seriously interferes with the signal characteristics, reducing modulation recognition accuracy. The existing methods passively improve the accuracy from the perspective of selecting appropriate signal features, lacking specialized preprocessing for suppressing multipath effects. So, the accuracy improvement of the designed modulation recognition models is limited, and the adaptability to environmental changes is poor. The method proposed in this paper actively utilizes common synchronous signals in underwater acoustic communication as detection signals to achieve passive time reversal without external signals and designs a passive time reversal-autoencoder to suppress multipath effects, enhance signals’ features, and improve modulation recognition accuracy and environmental adaptability. Firstly, synchronous signals are identified and estimated. Subsequently, a passive time reversal-autoencoder is designed to enhance power spectrum and square spectrum features. Finally, a modulation classification is performed using a convolutional neural network. The model is trained in simulation channels generated by Bellhop and tested in actual channels which are different from the training period. The average recognition accuracy of the six modulated signals is improved by 10% compared to existing passive modulation recognition methods, indicating good environmental adaptability as well.
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Jang, Junsu, and Florian Meyer. "Bayesian navigation in shallow water using passive acoustics." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A304. http://dx.doi.org/10.1121/10.0018938.
Повний текст джерелаАнотація:
Autonomous underwater vehicle (AUV) navigation relying on active acoustic sources causes noise pollution, while dead reckoning leads to a localization error that increases with time. Therefore, AUV navigation based on passive acoustics is appealing. However, for AUV navigation, extracting location information with passive acoustics is a challenging signal processing task. Due to the small form factor of an AUV as a sensing platform, only a single hydrophone or a small aperture hydrophone array can be used as an acoustic sensor. Furthermore, the acoustic signals originate from uncooperative sources. Here, we propose a Bayesian navigation approach for an AUV that exploits acoustic signals generated from sources of opportunity (SOOs) in a shallow water environment. The waveguide invariant (WI) parameter is estimated from cross-correlation coefficients of non-linearly transformed tonal signals of an SOO. It is assumed that the location information of the SOO is transmitted by an automatic identification system. Additionally, the range rate is inferred using the spectrum of cross-correlatedacoustic fields over a time interval. The WI parameter estimate, the range rate estimate, and inertial measurements are fused in a Bayesian parameter estimation approach. The navigation capability is demonstrated using simulated and real data from the SwellEx-96 experiment.
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Shakhtarin, B. I., V. V. Chudnikov, and R. M. Dyabirov. "Methods of Frequency Synchronization of OFDM Signals in an Underwater Acoustic Channel." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 4 (127) (August 2019): 62–70. http://dx.doi.org/10.18698/0236-3933-2019-4-62-70.
Повний текст джерелаАнотація:
Application of signals with orthogonal frequency division multiplexing in underwater communication systems allows efficient use of the information transfer channel bandwidth and thereby increase the carrying capacity of the system. Among the main distinguishing features of the underwater channel there are the relatively low speed of sound propagation in water, multiple reflections from the water surface and the bottom of the reservoir and the Doppler effect, which leads to compression / stretching of the signal in time. The model of the underwater acoustic channel was developed on the assumption that the signal at the receiver input is a superposition of the signals which are copies of the transmitted signal, but passed through different paths from the transmitter. Each signal has its own amplitude, time delay and degree of compression / stretching in time. For correct demodulation of the orthogonal frequency division of the channel-signal, the receiver must first perform time and frequency synchronization. Time synchronization is performed to determine the beginning of the packet and the symbols’ boundaries, and frequency synchronization is necessary for matching the receiver and transmitter sampling frequency to eliminate interchannel interference.For frequency synchronization in a hydroacoustic channel of orthogonal frequency division type, either the preambles invariant to Doppler effect or pilot components of the channel of the orthogonal frequency division type are used. The method based on the synchronization preamble and on a bank of matched filters uses a non-invariant to the Doppler effect preamble at the beginning of the packet. Each filter is matched with a preamble having compression / stretching in time. The autocorrelation method assumes that two identical symbols are included in the transmitted data block for signals with orthogonal frequency division multiplexing, which are used to estimate the scale of signal stretching / compression. The conclusions on the advantages of using orthogonal frequency division multiplexing in an underwater acoustic channel are given.