Underwater Signal Research Articles

Underwater sound generated by motor vehicle traffic in an underwater tunnel

Underwater acoustic and environmental measurements were made adjacent to the Monitor-Merrimac Memorial Bridge-Tunnel in the James River in Virginia during a field experiment in April 2019. The observed sound field exhibited short time-scale and tidal time-scale variability due to passing marine vessels and tidally driven currents in the estuary; most interestingly, the sound field exhibited variability on a diurnal time scale, closely correlated to the temporal distribution of vehicular traffic in the underwater tunnel. During morning and evening rush-hour traffic periods, the amplitude of the traffic-induced underwater signal exceeded 10 dB at 40 Hz. Additional physics-based modeling and direct observation are required to verify the hypothesis that the signal is associated with the traffic-induced vibration in the tunnel roadbed.

Signal Improvement for Underwater Measurement of Metal Samples Using Collinear Long-Short Double-Pulse Laser Induced Breakdown Spectroscopy

A collinear long-short double-pulse laser induced breakdown spectroscopy (LS-DP-LIBS) is employed to detect the underwater metal samples. The emission spectra, time-resolved signal, plasma images and sound characteristics of plasma shockwave are experimentally investigated in this work. The results show that the underwater signal of Al, Cu and Fe spectral lines are significantly improved by collinear LS-DP-LIBS with inter-pulse delay of 35 us. The mechanism of the signal improvement is considered to be the pre irradiation effect of long pulse laser beam. In the collinear LS-DP-LIBS method, the long pulse firstly generates a cavitation bubble in water and provides the gaseous environment. Then the short pulse generates the plasma from sample surface. The present experiments show that the collinear LS-DP-LIBS method has acquired significantly signal improvement in underwater measurement of metal samples. This new method has great potential in deep-sea exploration using LIBS.

Underwater Image and Signal Processing

Underwater Image and Signal Processing

Solution of the Problem of Classification of Hydroacoustic Signals Based on Harmonious Wavelets and Machine Learning

Two types of real hydroacoustic signals of whales are classified based on the harmonic wavelet transform (HWT) coefficients (fast implementation), windowed Fourier transform (FT) (spectrogram), and conventional FT using the k-NN algorithm. The accuracy of the classification is estimated for various signal-to-noise ratios (SNRs). In order to reduce the dimension of the feature space during classification using the k-NN algorithm, the use of the modulo N reduction method is proposed. The efficiency of the use of harmonic wavelets in the classification of complex nonstationary signals is experimentally proved. The applicability of speech processing methods for the classification of underwater bioacoustic signals is confirmed. The discussed methods are initially developed taking into account the characteristics of human speech, but, nevertheless, showed good results even without being tuned to the characteristics of the classified signals. The problem of classifying two types of whales by the sounds they make using a neural network is solved.

Parameter estimation of underwater impulsive noise with the Class B model

The statistical characteristic of ocean ambient noise plays an important role in developing underwater signal processors. Considering that the noise in shallow water shows the impulsive nature, non-Gaussian noise models are usually applied to model the ocean ambient noise. In this study, the ocean ambient noise is modelled by using the Middleton Class B model, which can be decomposed into Gaussian and non-Gaussian models. Then, the parameters of Class B model are estimated based on the least-square estimation method, which can be deduced by using the characteristic function of the Middleton Class B model. The processing results of simulated data and real data indicate that the presented method well estimates the parameters of Class B modelled noise. Besides, it further shows that the Middleton Class B model is suitable for modelling the impulsive noise in shallow water.

A recurrent plot based stochastic nonlinear ray propagation model for underwater signal propagation

A stochastic nonlinear ray propagation model is proposed to carry out an exploration of the nonlinear ray theory in underwater signal propagation. The recurrence plot method is proposed to quantify the ray chaos and stochastics to optimize the model. Based on this method, the distribution function of the control parameter δ is derived. Experiments and simulations indicate that this stochastic nonlinear ray propagation model provides a good explanation and description on the stochastic frequency shift in underwater signal propagation.

A new insight on a mechanism of airborne and underwater sound of a drop impacting a liquid surface

We report on an experimental study of the impact of a water drop on a liquid surface in the regime of the so-called irregular entrainment. The hydrodynamics of the phenomenon has been correlated finely to the features of the acoustic signal, both underwater and in the air, thanks to the synchronization of images and sounds in a home-made setup. If the origin of the acoustic signal is known to be caused by the capture of a bubble during the hydrodynamic flow following the impact, for the first time, a new mechanism responsible for the formation of the air bubble is highlighted. The latter is caused by the closing, like a liquid zipper, of the cavity induced by the retraction of the Rayleigh jet, by a secondary droplet detached from this jet. The comparison of the experimental data with the Minnaert model and plane wave theories reveals: (i) the time-dependence of the instantaneous oscillation frequency, (ii) a dominant frequency about 30% higher than the Minnaert prediction, (iii) a higher damping characteristic time, and (iv) a two orders of magnitude higher water–air transmission coefficient. All these results can be explained by the proximity of the bubble to the air–water interface, and by the too small dimensions of the tank to avoid underwater echoes in the measured underwater signal.

Image descattering and absorption compensation in underwater polarimetric imaging

Underwater image contrast degrades and color distorts due to wavelength attenuation caused by scattering and absorption, and the attenuation rate of different wavelengths is significant different in variety of underwater environment, which leads to the satisfying underwater image restoration very difficult. We propose a novel underwater polarimetric image restoration method that consists of descattering and absorption compensation, which particularly takes into account wavelength- and scenedepth- dependent absorption existed in underwater signal light that is ignored by current polarimetric descattering methods. In the descattering step, an automatic attenuation difference map based background region selection strategy is presented, which can estimate background light without considering the no-object region exists or not. In the absorption compensation step, a novel absorption compensation constraint strategy is introduced into the color restoration algorithm for the detected signal light. The strategy relies on well-preserved color component and an absorption prior constraint. Extensive experiments on real-world underwater images demonstrate that the proposed method is more effective than several previous underwater image restoration or enhancement works.

Underwater Detection Signal Based on LM-BP Neural Network Algorithm

The algorithm of underwater signal detection based on LM-BP neural network has a very broad application prospect. It is an important means of underwater background weak signal detection and a good method of phase space reconstruction. In this paper, the band-pass LM algorithm combined with BP neural network is used to process the underwater signal, which can effectively filter the magnetic noise in the underwater environment and extract the characteristics of the underwater detection signal. This paper first analyzes the time-frequency characteristics of the underwater detection signal, then studies the BP neural network of LM algorithm, and studies the design of band-pass filter, finally analyzes the application example based on LM-BP algorithm.

Optical 4D signal detection in turbid water by multi-dimensional integral imaging using spatially distributed and temporally encoded multiple light sources.

We propose an underwater optical signal detection system based on multi-dimensional integral imaging with spatially distributed multiple light sources and four-dimensional (4D) spatial-temporal correlation. We demonstrate our system for the detection of optical signals in turbid water. A 4D optical signal is generated from a three-dimensional (3D) spatial distribution of underwater light sources, which are temporally encoded using spread spectrum techniques. The optical signals are captured by an array of cameras, and 3D integral imaging reconstruction is performed, followed by multi-dimensional correlation to detect the optical signal. Inclusion of multiple light sources located at different depths allows for successful signal detection at turbidity levels not feasible using only a single light source. We consider the proposed system under varied turbidity levels using both Pseudorandom and Gold Codes for temporal signal coding. We also compare the effectiveness of the proposed underwater optical signal detection system to a similar system using only a single light source and compare between conventional and integral imaging-based signal detection. The underwater signal detection capabilities are measured through performance-based metrics such as receiver operating characteristic (ROC) curves, the area under the curve (AUC), and the number of detection errors. Furthermore, statistical analysis, including Kullback-Leibler divergence and Bhattacharya distance, shows improved performance of the proposed multi-source integral imaging underwater system. The proposed integral-imaging based approach is shown to significantly outperform conventional imaging-based methods.

Line Spectrum Enhancement of Underwater Acoustic Signals Using Kalman Filter

To detect weak underwater acoustic signals radiated by submarines and other underwater equipment, an effective line spectrum enhancement algorithm based on Kalman filter and FFT processing is proposed. The proposed algorithm first determines the frequency components of the weak underwater signal and then filters the signal to enhance the line spectrum, thereby improving the signal-to-noise ratio (SNR). This paper discussed two cases: one is a simulated signal consisting of a dual-frequency sinusoidal periodic signal and Gaussian white noise, and the signal is received after passing through a Rayleigh fading channel; the other is a ship signal recorded from the South China Sea. The results show that the line spectrum of the underwater acoustic signal could be effectively enhanced in both cases, and the filtered waveform is smoother. The analysis of simulated signals and ship signal reflects the effectiveness of the proposed algorithm.

Percussive underwater signaling in wild gray seals

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Frequency Invariant Beamforming for a Small-Sized Bi-Cone Acoustic Vector-Sensor Array.

In this work, we design a small-sized bi-cone acoustic vector-sensor array (BCAVSA) and propose a frequency invariant beamforming method for the BCAVSA, inspired by the Ormia ochracea’s coupling ears and harmonic nesting. First, we design a BCAVSA using several sets of cylindrical acoustic vector-sensor arrays (AVSAs), which are used as a guide to construct the constant beamwidth beamformer. Due to the mechanical coupling system of the Ormia ochracea’s two ears, the phase and amplitude differences of acoustic signals at the bilateral tympanal membranes are magnified. To obtain a virtual BCAVSA with larger interelement distances, we then extend the coupling magnified system into the BCAVSA by deriving the expression of the coupling magnified matrix for the BCAVSA and providing the selecting method of coupled parameters for fitting the underwater signal frequency. Finally, the frequency invariant beamforming method is developed to acquire the constant beamwidth pattern in the three-dimensional plane by deriving several sets of the frequency weighted coefficients for the different cylindrical AVSAs. Simulation results show that this method achieves a narrower mainlobe width compared to the original BCAVSA. This method has lower sidelobes and a narrower mainlobe width compared to the coupling magnified bi-cone pressure sensor array.

A Modified Differential Beamforming and Its Application for DOA Estimation of Low Frequency Underwater Signal

Conventional beamforming (CBF) will lose its directivity once the signal frequency is much lower than the designed frequency of the array. In this paper, a modified differential beamforming (MDBF) method is proposed to deal with the underwater acoustic signal at low frequency band and then its application for direction of arrival (DOA) estimation with deconvolution technique is given. The MDBF proposed in this paper is a variation of the conventional differential beamforming (DBF) method and it constructs an optimization problem with an additional robustness constraint. The solution of MDBF solved via Lagrange multiplier technique owns a closed-form similar to that of DBF except two additional loading terms. Simulations with planar arrays show that for the frequency band where CBF owns an almost omnidirectional beampattern, MDBF outperforms DBF in robustness at the cost of some directivity loss. The application of MDBF for DOA estimation is demonstrated with both simulated and experimental data. The results show that the proposed MDBF outperforms both DBF and CBF in DOA estimation at low frequency band.

Detect Black Box Signals with Enhanced Spectrum and Support Vector Classifier

When the plane crash into the sea and the search and rescue team search the location black box, we need to detect a specific acoustic signal in the ocean. The underwater acoustic channel has several distinct features: multipath propagation, high attenuation, and sound velocity varying as a function of depth and temperature, which make the detection of underwater signals very difficult. This paper proposes a new method in the marine signal detection and discrimination based on adaptive line enhancer and support vector machine classifier, which can provide a new idea for marine black box search. The adaptive line enhancer of the least mean square algorithm can detect narrowband signals hidden in wideband noise, while the support vector machine classifier maps signals to high-dimensional space and screens out hyperplanes separating different signals from machine data through previous data. Experimental data show that this method can bring a very high recognition rate.

Field Demonstrations of Wide-Beam Optical Communications Through Water–Air Interface

The connectivity of undersea sensors and airborne nodes across the water-air interface has been long sought. This study designs a free-space wireless laser communications system that yields a high net data rate of 850 Mbit/s when perfectly aligned. This system can also be used for an extended coverage of 1963 cm 2 at the receiver while sustaining a net data rate of 9 Mbit/s over 10 m. The utility of this system was verified for direct communications across the water-air interface in a canal of the Red Sea based on a pre-aligned link as well as a diving pool under a mobile signal-searching mode. The canal deployment measured a real-time data rate of 87 Mbit/s when pre-aligned in turbid water over 50 min, which confirms the system robustness in harsh water environments. In the pool deployment, a drone configured with a photodetector flew over the surface of the water and recorded the underwater signals without a structure-assisted alignment. Using a four-quadrature amplitude-modulated orthogonal frequency-division multiplexing (4-QAM-OFDM) modulation scheme provided a net data rate of 44 Mbit/s over a 2.3-m underwater and 3.5-m air link. The results validated the link stability and mitigated problems that arise from misalignment and mobility in harsh environments, which paves the way for future field applications.

SHiPCC—A Sea-going High-Performance Compute Cluster for Image Analysis

Marine image analysis faces a multitude of challenges: data set size easily reaches Terabyte-scale; the underwater visual signal often is impaired to the point where information content becomes negligible; interpreters are scarce and can only focus on subsets of the available data due to the annotation effort involved. Solutions to speed-up the analysis process have been presented in the form of semi-automation with artificial intelligence methods like machine learning. But the algorithms employed to automate the analysis commonly rely on large-scale compute infrastructure. So far, such an infrastructure has only been available on-shore. Here, a mobile compute cluster is presented to bring big image data analysis capabilities out to sea. The Sea-going High-Performance Compute Cluster (SHiPCC) units are mobile, robustly designed to operate with impure ship-based power supplies and based on off-the-shelf computer hardware. Each unit comprises of up to eight compute nodes with graphics processing units for efficient image analysis and an internal storage to manage the big image data sets. The first SHiPCC unit has been successfully deployed at sea. It allowed to extract semantic and quantitative information from a Terabyte-sized image data set within 1.5 hours (a relative speedup of 97\% compared to a single four-core CPU computer). Enabling such compute capability out at sea allows to include image-derived information into the cruise research plan, for example by determining promising sampling locations. The SHiPCC units are envisioned to generally improve the relevance and importance of optical imagery for marine science.

Underwater Signal Analysis in the Modulation Spectrogram with Time-Frequency Reassignment Technique

Underwater Signal Analysis in the Modulation Spectrogram with Time-Frequency Reassignment Technique

Short-Range Signatures of Explosive Sounds in Shallow Water Used for Seabed Characterization

Small explosions were used as sound sources in the Seabed Characterization Experiment conducted in spring 2017 in the New England Mud Patch, a shallow-water region with a depth of approximately 75 m. The sources were U.S. Navy Signal Underwater Sound (SUS) Mk 64 charges, which contained 31.18 g of the explosive 2,4,6-Trinitrophenylmethylnitramine, commonly referred to as Tetryl. Source recordings were obtained by two hydrophones deployed from the same ship that deployed the SUS. The recordings were analyzed for bubble period, energy spectral density, and the variability of these parameters, and compared to previous results from the literature, including the prediction of a historic spectral model, and a new semiempirical time-domain model assembled using measured data from the literature. The new model describes the source level measurements in the 25–275-Hz band and in the 400-Hz octave band to within 0.5 dB, and agrees with similar measurements from the literature to within 0.6 dB. The standard deviation of the band-limited source levels was found to be about 1 dB, some of which is ascribed to uncertainty and variation in the source-to-receiver distance. The observed source level variation is similar to previously reported values.

A biosonar model of finless porpoise (Neophocaena phocaenoides) for material composition discrimination of cylinders.

Research into the physical mechanism of odontocetes biosonar has made great progress in the past several decades, especially on wave propagation and biosonar beam formation in the foreheads of odontocetes. Although a number of experimental studies have been performed, the physical mechanism of odontocetes underwater target discrimination has not yet been fully understood. Previous research has experimentally studied the finless porpoise's target discrimination using cylinders different in material [Nakahara, Takemura, Koido, and Hiruda (1997). Mar. Mamm. Sci. 13(4), 639-649]. The authors proposed a computed tomography based finite element biosonar model to simulate the detailed process of a finless porpoise click emission and target detection in order to gain a further understanding of the underlying physical mechanism. The numerical solutions of resonance features of both steel and acrylic cylinders in this study are very consistent with the analytic solutions. Furthermore, the simulated outgoing clicks and echoes match the experiment results measured by Nakahara et al. The beam patterns of the scattered field were extracted and the resonance features of cylinders in different materials were analyzed. This method in this study could be used to study some other odontocetes that are inaccessible for experimental work and could also provide physical information for intelligent biomimetic underwater signal processors design.