Underwater Acoustic Signals Research Articles

Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data.

Detecting and classifying ships based on radiated noise provide practical guidelines for the reduction of underwater noise footprint of shipping. In this paper, the detection and classification are implemented by auditory inspired convolutional neural networks trained from raw underwater acoustic signal. The proposed model includes three parts. The first part is performed by a multi-scale 1D time convolutional layer initialized by auditory filter banks. Signals are decomposed into frequency components by convolution operation. In the second part, the decomposed signals are converted into frequency domain by permute layer and energy pooling layer to form frequency distribution in auditory cortex. Then, 2D frequency convolutional layers are applied to discover spectro-temporal patterns, as well as preserve locality and reduce spectral variations in ship noise. In the third part, the whole model is optimized with an objective function of classification to obtain appropriate auditory filters and feature representations that are correlative with ship categories. The optimization reflects the plasticity of auditory system. Experiments on five ship types and background noise show that the proposed approach achieved an overall classification accuracy of 79.2%, which improved by 6% compared to conventional approaches. Auditory filter banks were adaptive in shape to improve accuracy of classification.

Statistical Characterization of Acoustic Signals Using 1D Wavelet Transforms with Applications in Acoustical Oceanography

The paper summarizes the research carried out at the University of Crete and the Foundation for Research and Technology-HELLAS aiming at the statistical characterization of underwater acoustic signals and their subsequent use for geoacoustic inversions and applications in ocean acoustic tomography. In these applications, an acoustic signal recorded in the marine environment due to some source is used as the carrier of information on the physical parameters of the environment. Statistical characterization of acoustic signals is a pre-processing technique aiming at the definition of signal observables, to be used as input data of appropriately defined inverse problems aiming at the estimation of critical parameters of the marine environment. The statistical characterization scheme was introduced as a way to define signal observables especially in cases that typical observables such as ray arrivals or modal arrivals cannot be identified in the recorded signals. Moreover, the setting of the associated inverse problem requires just a single recording device, which renders its application practically and relatively cheap in comparison with signal inversion methods requiring reception at an array of hydrophones. The characterization scheme is based on a wavelet transform of the signal at various levels, followed by the statistical description of the wavelet sub-band coefficients. It is shown that A-stable symmetric distributions are capable of defining the statistics of these coefficients, the characteristic parameters of which are the observables of the signal to be exploited for the inversions. As the inverse problems associated with the sought applications are formulated as optimization processes, an objective function to be used as a similarity measure is defined, which in the case of the statistical characterization method is the Kullback–Leibrer Divergence (KLD), capable of comparing probability density functions. The inversion processes are performed by means of neural networks or genetic algorithms, and the performance of the combined signal characterization and inversion method has been tested with simulated and real data. It is shown, that the method works well especially with noise-free or denoised signals.

Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient.

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.

RSHSC-Routing Algorithm Based on Simplified Harmony Search and Coding for UWSNs

With the development of wireless networks and increasingly interest of people in underwater resources and environment, UWSNs are being paid more and more attention. Because of the characteristics of underwater channel and acoustic signal, the protocols used in the terrestrial networks cannot be directly used in UWSNs. In this paper, a reliable and energy-efficient routing protocol based on SHS and coding, called RSHSC, is proposed. Firstly, regular nodes are assigned to cluster heads according to simplified harmony search algorithm. Secondly, partial network encoding is introduced and the next two-hop information is considered when data packets are transmitted to sink nodes from the source node. Only the best next-hop forwards data packets. All data packets from neighbor nodes are used for decoding. Thirdly, two schemes of updating routing are designed and compared. Lastly, extensive simulations prove RSHSC is effective in improving reliability and decreasing energy consumption.

Compressed sensing and recovery of underwater acoustic signal in Internet of Things

Telemonitoring and communications technologies of underwater information is inseparable from the process of sampling and transmitting of data. Conventional methods often fail in energy efficiency to obtain such a huge data in Internet of Things (IoT), compressed sensing (CS) provides a new perspective to solve the problem. Unfortunately, the underwater acoustic signal is non-sparse in the time domain and thus the current CS methods cannot be used directly. This study adopts the fast Fourier transform (FFT) based dictionary-matrix for sparse representation. Then we design an approach based on an approximated $l0$ (AL0) norm at the receiving terminal, to search the sparse solution via the filtered steepest descent method and projections. The sparse estimation is used for reconstruction of collected data, combing with the previously used measurement matrix and dictionary-matrix. Experimental results confirm the superior performances of the strategies of the proposed method than the traditional methods including matching pursuit (MP) and orthogonal matching pursuit (OMP) methods. Telemonitoring and communications technologies of underwater information is inseparable from the process of sampling and transmitting of data. Conventional methods often fail in energy efficiency to obtain such a huge data in Internet of Things (IoT), compressed sensing (CS) provides a new perspective to solve the problem. Unfortunately, the underwater acoustic signal is non-sparse in the time domain and thus the current CS methods cannot be used directly. This study adopts the fast Fourier transform (FFT) based dictionary-matrix for sparse representation. Then we design an approach based on an approximated $l0$ (AL0) norm at the receiving terminal, to search the sparse solution via the filtered steepest descent method and projections. The sparse estimation is used for reconstruction of collected data, combing with the previously used measurement matrix and dictionary-matrix. Experimental results confirm the superior performances of the strategies of the proposed method than the traditional methods includ...

Compressed Sensing of Underwater Acoustic Signals via Structured Approximation $l_0$-Norm

The underwater wireless sensor networks enable the telemonitoring and communications technologies of underwater information play important roles in the gathering process of scientific data in collaborative monitoring missions. However, to design such a system, several aspects must be considered to make fewer resources required, such as energy efficiency, miniaturization, the required functionality, etc. Conventional methods of data compression and reconstruction fail in energy efficiency. Different from the conventional compression methods, compressive sampling (CS) provides a new perspective to compress huge data with low energy consumption. Unfortunately, the recordings of underwater acoustic signal (UAS) are nonsparse in time domain. Hence, the current CS methods cannot be used directly for compression and reconstruction of UAS. This study adopts the wavelet-transform-based dictionary matrix to build a framework for sparse representation; then, introduces an approach based on structured approximation $l_0$ (SAL0) norm, which is designed by exploring and exploiting the correlation structure of UAS. The proposed method searches the optimal sparse solution via steepest descent method and then projects the solution to its feasible set. Combing with the compression matrix and dictionary matrix, the estimation of SAL0 method is used for reconstructing nonsparse UAS. Experimental results confirm its recovery quality for practical use, and a clear improvement than the traditional methods including $l_1$ (L1) and orthogonal matching pursuit methods.

Blind equalization and automatic modulation classification of underwater acoustic signals

It is useful to passively characterize the underwater acoustic communications environment for a variety of purposes, including interference avoidance and enforcement of restrictions protecting marine mammals. Automatically determining the modulation of a received waveform can permit sonar or communications operations within the same bandwidth with a minimum of collisions, and it can identify a particular system operating outside its permitted regime. The characterization system needs to both determine the modulation and an unknown, time-varying channel impulse response since the transmitter and receiver are not coordinating. In this work, we demonstrate the use of blind equalization along with Convolutional Neural Networks for automatic classification of underwater signals. Our current research focuses on classification of constant modulus signals and demonstrates an approximate 30 percent improvement in modulation classification, compared to approaches without equalization, and a significant reduction in the amount of data needed for training. We considered BPSK, QPSK, MSK, FSK and 8-PSK modulations using simplified synthetic channels simulated via MATLAB to demonstrate our results. Future work is aimed at demonstrating classification improvement using realistic channel models simulated via the Sonar Simulation Toolkit, real underwater channels gathered from data collects, and additional underwater acoustic signal types.

Underwater acoustics array spatial reversal convolution and beamforming

A novel underwater acoustic array signal processing method based on spatial reversal and convolution, aiming at improving detection performance, is proposed to meet the engineering application demand of the underwater acoustic array detection system. The theoretical and mathematical model of the proposed method is deduced and presented in detail. Based on the extended covariance matrix of the physical array, the array sensitivity equalization and the noise suppression technique of the underwater acoustic array are studied. The additional gain provided by sensitivity equalization and noise suppression techniques is analyzed and exploited. Computer modelling and various numerical simulations are applied to demonstrate the proposed method’s effectiveness. At-sea experimental data are processed to test the performance in real underwater acoustic environment. These results indicate that the proposed array processing method can effectively improve the detection ability. The proposed techniques in this paper provide an insightful way to improve the detection performance of the underwater acoustic array.

Airborne and underwater acoustic repertoire of hooded seals (Cystophora cristata): Cornerstone for acoustic monitoring

The hooded seal is a migratory species inhabiting the North Atlantic. They whelp and breed during mid- to late March on pack ice near Jan Mayen Island, in the Davis Strait, off the northeastern Newfoundland coast, and in the Gulf of St. Lawrence. After breeding, hooded seals return to the pack ice off eastern Greenland to moult during June and July, and then they disperse broadly for summer and fall before returning to their respective breeding areas. Passive acoustic monitoring conducted over spatial scales consistent with known and potential habitat of the hooded seal could add insight into seasonal, diel, and spatial occurrence patterns of this species. To better characterize its acoustic repertoire (notably underwater calls), airborne and underwater acoustic signals of hooded seals were recorded during their breeding season on the pack ice in the Gulf of St. Lawrence from 12 to 17 March 2018. Hood and septum noises were the predominant sounds heard from males on the ice surface. Hooded seal underwater acoustic repertoire is larger and more diverse than has been previously described. Using the dataset from an extended acoustic monitoring program along Canada’s East Coast, some clues are provided about the seasonal distribution of hooded seals.

Measurements of modal attenuation using broadband sources in the New England Mud Patch

Measurements of underwater acoustic signals were made on a bottom-mounted horizontal line array during the Seabed Characterization Experiment (SCEx) in the New England Mud Patch south of Martha’s Vineyard in about 70 meters of water. The signals were generated by SUS (Signals, Underwater Sound) charges detonated at various locations in the experimental area at a depth of 18 m, during nearly-isovelocity conditions. The broadband signals were analyzed for modal arrival time and amplitude using time-frequency techniques. Ratios of modal amplitudes at the different hydrophones were used to estimate the modal attenuation coefficients. Hence, these estimates are independent of any uncertainty in the frequency-dependent source level of the SUS charges. These coefficients are directly related to the depth-dependent sediment attenuation profile. A sensitivity study was performed to understand how the modes sample the different layers and provides estimates of resolution kernels. Posteriori error analysis provides averages and standard deviations for the estimate of sediment attenuation as function of depth. The frequency bands of interest range from 10 Hz to 200 Hz for modes 1 to 4. We will compare our estimates of sediment attenuation with historical measurements. [Work supported by Office of Naval Research.]

The Study of Ocean Ambient Noise Reconstruction in Low and Medium Frequency Bands

Ocean ambient noise is the general background of the sea, the sources of which is highly variable, the generated noise signal is random, it changes with time and space. It is usually considered that ocean ambient noise is a special random process with certain power spectrum, so the broadband stationary random process can be used for fitting the continuous spectral noise. This paper carried out statistical analysis on sample data obtained by continuous observation in one day, and then the statistic characteristics of ambient noise frequency spectrum in low and medium frequency bands can be calculated. Generating filter with specific frequency response based on the probability density function of sound pressure level in each frequency randomly, and ocean ambient noise is reconstructed effectively by Gaussian white noise passing the filter above. The simulation results have important implication for low and medium frequency underwater acoustic signal analysis in lab conditions, and it is helpful to test the performance of platform in the oceanic background.

Parameter Estimation for Class A Modeled Ocean Ambient Noise

A Gaussian distribution is used by all traditional underwater acoustic signal processors, thus neglecting the impulsive property of ocean ambient noise in shallow waters. Undoubtedly, signal processors designed with a Gaussian model are sub-optimal in the presence of non-Gaussian noise. To solve this problem, firstly a quantile-quantile (Q-Q) plot of real data was analyzed, which further showed the necessity of investigating a non-Gaussian noise model. A Middleton Class A noise model considering impulsive noise was used to model non-Gaussian noise in shallow waters. After that, parameter estimation for the Class A model was carried out with the characteristic function. Lastly, the effectiveness of the method proposed in this paper was verified by using simulated data and real data.

A New Underwater Acoustic Signal Denoising Technique Based on CEEMDAN, Mutual Information, Permutation Entropy, and Wavelet Threshold Denoising.

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.

Compressive Sampling and Reconstruction of Acoustic Signal in Underwater Wireless Sensor Networks

The process of gathering of scientific data plays an important role in telemonitoring and communications technologies of underwater information. However, to obtain such a huge data in underwater-wireless-sensor-networks, conventional methods often fail in energy efficiency. Compressive sampling (CS) provides a new perspective to solve the problem. Unfortunately, the underwater acoustic signal is non-sparse in the time domain and the current CS methods cannot be used directly. This paper adopts the discrete cosine transform-based dictionary-matrix for sparse representation. In addition, the measurement matrix is optimized via the steepest descent method for more efficient sampling. Then, we introduce an approach based on an approximated l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm at the receiving terminal, to search the sparse solution via the steepest descent method and projections. Combing with the measurement matrix and dictionary-matrix, the sparse estimation is used for reconstruction. Experimental results confirm the superior performances of the strategies of the proposed compressive sampling and reconstruction than those of the traditional measurement matrix and reconstruction methods, including matching pursuit and orthogonal matching pursuit methods.

A New Hybrid Model Based on Fruit Fly Optimization Algorithm and Wavelet Neural Network and Its Application to Underwater Acoustic Signal Prediction

The local predictability of underwater acoustic signal plays an important role in underwater acoustic signal processing, and it is the basis of nonstationary signal detection. Wavelet neural network model, with the advantages of both wavelet analysis and artificial neural network, makes full use of the time-frequency localization characteristics of wavelet analysis and the self-learning ability of artificial neural network; however, this model is prone to fall into local minima or creates convergence. To overcome these disadvantages, a new hybrid model based on fruit fly optimization algorithm (FOA) and wavelet neural network (WNN) is proposed in this paper. The FOA-WNN prediction model is constructed by optimizing the weights and thresholds of wavelet neural network, and the model is applied to underwater acoustic signal prediction. The experimental results show that the FOA-WNN prediction model has higher prediction accuracy and smaller prediction error, compared with wavelet neural network prediction model and BP neural network prediction model.

Modulation recognition of non-cooperation underwater acoustic communication signals using principal component analysis

The modulation classification of the non-cooperation underwater acoustic (UWA) communication signals is extremely challenging due to the adverse UWA channel transmission characteristics and low signal to noise ratio (SNR), which lead to considerable impairments of the signal features. In this paper, the principal component analysis (PCA) is proposed for efficient extraction of the power spectra and square spectrum features of UWA signals at the presence of multipath, Doppler, and noise induced in UWA channels. The employment of PCA enables extraction of the principal components associated with different modulation mode as the input vector of classier, thus reducing the feature dimension and suppressing the influence of UWA channels and environmental noise. With the features obtained by PCA, an artificial neural network (ANN) classifier is adopted for modulation recognition of UWA communication signals. The experimental modulation classification results obtained with field signals in 4 different underwater acoustic channels show that the proposed PCA based modulation recognition method outperforms the classifier using classic features in terms of classification performance and noise tolerance.

Sustainable model for high signal to noise ratio to measure underwater acoustic signal using Acoustic Doppler Velocimeter

The objective of this research is to identify the optimum region in the underwater of flume, an artificial channel setup, to measure the acoustic signal. Acquiring the signal with high Signal to Noise Ratio is essential. Utilizing the Response Surface Methodology (RSM) with three factors Central Composite Design (CCD), this paper demonstrates the sustainable model of characterizing the Signal to Noise Ratio (SNR) through second order quadratic model in measuring the acoustic signal through Acoustic Doppler Velocimeter (ADV). Over all model fitness test of the suggested Design is justified by Degrees of freedom (DF). Highly Influenceable parameters of Signal to Noise Ratio are recognized using Analysis of Variance (ANOVA). The design for optimum region to investigate and acquire the signal for processing is acknowledged through F-Statistics. The multicolinearity of parameters is studied by Variance Inflation Factor, brought uncontrolled parameter such as Signal to Noise Ratio and Velocity of the measured signal to under control.

De-noising algorithm for SNR improvement of underwater acoustic signals using CWT based on Fourier Transform

De-noising algorithm for SNR improvement of underwater acoustic signals using CWT based on Fourier Transform

Power Allocation for OFDM Over Multi-scale Multi-lag Channels

Underwater acoustic (UWA) signal is wideband in nature and suffers doubly selective fading in time and frequency. In the presence of relative motion, a typical UWA channel can be described by a multiscale multilag (MSML) model. To compensate the scaling effect of UWA channels, many UWA communication systems employ a signal-resampling operation by a single scale at the front end of the receiver. For orthogonal frequency division multiplexing systems, since the path scales are different from each other, the resampling can alleviate but not clean out the intercarrier interference (ICI) resulting from the MSML channel. In this paper, we first describe the resampled signal by an equivalent MSML model with residual time scales, which are much smaller than that of the original MSML channel. With the given statistical parameters of the equivalent MSML channel, the closed-form expressions of the average subchannel gains and ICI coefficients are derived. Based on the ICI coefficients, we derive the analytical expression of a lower bound on the average achievable rate of the system as a function of the power allocation. We then investigate the power allocation to improve the lower bound on the achievable rate. Our key idea is that, on subcarriers whose ICIs dominate the additive noise, nothing is transmitted to avoid low energy efficiency. After proving that the ICI coefficient increases monotonically as the subcarrier frequency rises, we propose an ON–OFF uniform power allocation method, in which subcarriers with odd indices keep inactive starting from a certain index to avoid ICI. The performance of the proposed power allocation is evaluated via numerical simulations, whose results show that the proposed method outperforms the existing methods when the residual time scales cannot be neglected.

Underwater Source Localization Using TDOA and FDOA Measurements With Unknown Propagation Speed and Sensor Parameter Errors

Underwater source localization problems are complicated and challenging: a) the sound propagation speed is often unknown and the unpredictable ocean current might lead to the uncertainties of sensor parameters (i.e. position and velocity); b) the underwater acoustic signal travels much slower than the radio one in terrestrial environments, thus resulting in a significantly severe Doppler effect; c) energy-efficient techniques are urgently required and hence in favour of the design with a low computational complexity. Considering these issues, we propose a simple and efficient underwater source localization approach based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, which copes with unknown propagation speed and sensor parameter errors. The proposed method mitigates the impact of the Doppler effect for accurately inferring the source parameters (i.e. position and velocity). The Cramer-Rao lower bounds (CRLBs) for this kind of localization are derived and, moreover, the analytical study shows that our method can yield the performance that is very close to the CRLB, particularly under small noise. The numerical results not only confirm the above conclusions but also show that our method outperforms other competing approaches.