Sound is widely used to sense and understand the oceans. Here's a look at the latest research on understanding and interpreting sound signals in the ocean.
5 April, 2021
by Hari Vishnu
Sound is the primary medium of sensing and probing the oceans, because it is absorbed less in seawater than electromagnetic waves and thus travels longer distances . Even marine mammals including dolphins, whales and porpoises use acoustics for sensing, navigation and communication. The field of underwater acoustics has been around for a very long time, with a quote attributed to the famous Italian artist Leonardo Da Vinci  in 1490 that
"If you cause your ship to stop, and place the head of a long tube in water, and place the outer extremity to your ear, you will hear ships at great length from you."
- Leonardo Da Vinci, 15th century
Underwater acoustic sensing now makes up one of the key broad areas in oceanic engineering, and has been categorized into sub-fields such as sonar signal processing, acoustic propagation and underwater acoustic communication.
Our oceans are under-explored, and less than 20% of the Oceans are mapped to modern survey standards. One of the aims of the ongoing UN Decade of Ocean Sciences is to build a better map of the Ocean floor to modern standards (read more here). Thus, it is inevitable during this Decade that we develop improved technologies to probe and understand the Ocean. Sound-based sensing is likely to play an important part in this in the coming years.
With this in mind, the IEEE Journal of Oceanic Engineering (JOE) regularly publishes a number of articles in in this field. The October 2020 issue of IEEE JOE contained a number of such papers on underwater acoustics. The following are the titles and abstracts of these papers from the topics of Signal processing, Sonar processing, Underwater Acoustics and Underwater communication, representing the cutting edge of research on underwater acoustics.
Higher-Order-Statistics-Based Direction-of-Arrival Estimation of Multiple Wideband Sources With Single Acoustic Vector Sensor
A. Agarwal, M. Agrawal, and A. Kumar
Acoustic vector sensor (AVS) measures acoustic pressure as well as the acoustic particle velocity to estimate the acoustic intensity. The acoustic intensity, which is a vector quantity, represents the magnitude and direction of the active or propagating part of an acoustic field, thus indicating the direction-of-arrival (DOA) of the received signal. This article proposes higher order statistics (HOS)-based DOA estimation with a single acoustic vector sensor in the underdetermined case where the number of sources is more than the number of sensors constituting a single vector sensor. The use of HOS allows the increase in the number of degrees of freedom, thereby resulting in increase in uniquely identifiable sources. It has been established that in an ideal condition, a single AVS using fourth-order statistics is capable of uniquely identifying eight sources with not more than four sources in a single plane. The number of sources can be further increased by working with HOS. Additional advantages of the proposed algorithm, such as robustness against the presence of nonidentical Gaussian noise or spatially correlated Gaussian noise at each constituent sensor of a single AVS, improved performance over second-order statistics-based algorithm, and ability to identify multiple wideband sources, have also been demonstrated.
Underwater Position and Attitude Estimation Using Acoustic, Inertial, and Depth Measurements
E. K. Jørgensen, T. I. Fossen, T. H. Bryne, and I. Schjølberg
This article considers the problem of constructing an observer for estimating position, velocity, attitude, underwater wave speed, rate sensor bias, and accelerometer bias that has both proven stability and close-to-optimal performance with respect to noise. The observer takes pseudorange, pseudorange difference, depth, and inertial measurements as input, and has a cascade structure for which the equilibrium point is proven to be locally exponentially stable due to the singularities in the attitude representation. The design of the observer is based on the exogenous Kalman filter principle, in which estimators with proven stability provide a linearization point for a linearized Kalman filter, to achieve both proven stability and close-to-optimal noise properties. Experimental validation is provided, with ground truth values generated by a camera positioning system with millimeter accuracy. The observer is compared to an extended Kalman filter and to a nonimplementable linearized Kalman filter using the true state as the linearization point, and the estimation error is almost identical to the linearized Kalman filter using the true state as a linearization point.
Asynchronous Localization of Underwater Target Using Consensus-Based Unscented Kalman Filtering
J. Yan, H. Zhao, X. Luo, Y. Wang, C. Chen, and X. Guan
Most applications of underwater acoustic sensor networks (UASNs) rely on accurate location information of targets. However, the asynchronous clock, stratification effect, and strong-noise characteristics of underwater environment make target localization more challenging as compared with terrestrial sensor networks. This paper focuses on an asynchronous localization issue for underwater targets, subjected to the isogradient sound speed and noise measurements. A network architecture including surface buoys, sensors, and the target is first designed, where the clocks on sensors and the target are not required to be synchronized. To eliminate the effect of asynchronous clocks, we establish the relationship between the propagation delay and the position. Particularly, the ray tracing approach is adopted to model the stratification effect. Then, a localization optimization problem is formulated to minimize the sum of all measurement errors. To solve the localization optimization problem, a consensus-based unscented Kalman filtering (UKF) localization algorithm is proposed, where the convergence conditions and Cramer-Rao lower bounds are also given. Finally, simulation results reveal that the proposed localization approach can reduce the localization time by comparing with the exhaustive search method. Meanwhile, the consensus-based UKF localization algorithm can improve localization accuracy as compared with other works.
Investigation on Stochastic Model Refinement for Precise Underwater Positioning
S. Zhao, Z. Wang, K. He, Z. Nie, H. Liu, and N. Ding
More accurate and realistic stochastic model is required for high-precision underwater positioning. The common stochastic modeling procedure, assuming that the measurements are statistically independent in space and time and have same accuracy, is certainly not realistic. Any misspecification in the stochastic model may have a significant effect on underwater acoustic data processing. Taking the heteroscedastic space and time correlation into consideration, a novel stochastic modeling procedure based on an iterative minimum norm quadratic unbiased estimator method has been developed to improve underwater positioning accuracy. Testing results by experiment data show that the positioning accuracy can be superiorly improved from 1.226, 1.434, and 1.018 m based on a traditional stochastic model estimation scheme to 0.935, 0.336, and 0.190 m for three transponders when adopting the new method.
Sonar Signal Processing
Interpolation Kernels for Synthetic Aperture Sonar Along-Track Motion Estimation
D. C. Brown, I. D. Gerg, and T. E. Blanford
The formation of high-resolution synthetic aperture sonar (SAS) imagery requires accurate estimates of the sensor's trajectory. This is frequently accomplished using the displaced phase center antenna technique, which utilizes cross correlation of the signals received on successive pings. Accurate estimates of the sensor's ping-to-ping advance are then made by measuring the along-track spatial coherence of the scattered field. Unbiased advance-per-ping estimates require an accurate model for the spatial coherence of the scattered field. This model may be found by the application of the van Cittert-Zernike theorem to the problem of pulsed active sonar systems. In this paper, it is shown that the spatial coherence for a typical high-frequency SAS collection geometry is well approximated by a Gaussian whose width is proportional to the sensor's element size. Gaussian and quadratic along-track interpolation kernel performances are compared for a pair of at sea data collections. A relative image quality metric, based on image contrast, is defined to quantitatively assess the performance of the pair of interpolation kernels. In both tests, the use of an along-track estimator is shown to provide improved image quality. Also in both tests, the performance of the Gaussian kernel exceeds that of the quadratic kernel.
Robust Resolution of Velocity Ambiguity for Multifrequency Pulse-to-Pulse Coherent Doppler Sonars
C. Chi, H. Vishnu, K. T. Beng, and M. Chitre
Pulse-to-pulse coherent Doppler sonars are widely used to explore boundary layer characteristics of oceans. Multifrequency pulse-to-pulse coherent Doppler sonars have been proposed in the literature to tackle the velocity ambiguity problem faced by single-frequency systems. A robust algorithm for resolving the velocity ambiguity is crucial in such systems. This paper proposes a method based on the robust Chinese remainder theorem to resolve the velocity ambiguity for multifrequency pulse-to-pulse coherent Doppler sonars. We evaluate the proposed method for resolving the velocity ambiguity in terms of the trial fail rate. The simulations show that the proposed method achieves 1/8 trial failure rate as the reference method. A theoretical criterion is also derived to support the observation that in most cases our proposed method is more robust to estimation errors than the reference method.
Utilizing Orthogonal Coprime Signals for Improving Broadband Acoustic Doppler Current Profilers
C. Chi, H. Vishnu, K. T. Beng, and M. Chitre
Broadband acoustic Doppler current profilers (BBADCPs) are instruments that are widely used for measuring ocean currents. The ambiguity velocity of the conventional method used in BBADCPs must accommodate all possible measurement velocities. Unfortunately, allowing a high-ambiguity velocity results in a high measurement deviation in conventional BBADCPs. We propose a method to break through the limitation imposed by the ambiguity velocity to improve BBADCPs. Our proposed method involves designing an orthogonal coprime signal to replace the conventional transmit signal in BBADCPs. The proposed orthogonal coprime signal consists of two orthogonal subsignals, whose ambiguity velocities are designed to be low and coprime. Utilizing the coprime property, we then employ the robust Chinese remainder theorem to resolve the velocity ambiguity from the two independent measurements made via the two orthogonal subsignals. Simulations show that our proposed method decreases the standard deviation of measurement velocity by nearly three times, compared to the conventional method used in BBADCPs. Our simulations also show that the proposed method can yield a 12-dB improvement of signal-to-noise ratio over the conventional method. This can help increase the profiling range significantly.
Predicting False Alarm Rates for High-Resolution Antisubmarine Warfare Sonars in a Cluttering Environment Prone to False Alarm Rate Inflation
K. T. Hjelmervik, H. Berg, and T. S. Såstad
Operation of high-resolution, broadband, antisubmarine warfare sonars in littoral waters is challenging, since the presence of sea mounts, underwater ridges, and other topographic features causes increased false alarm rates. Two important contributors to the raised false alarm rate are the signal-processing induced phenomenon called false alarm rate inflation (FARI) and the presence of sonar clutter, also referred to as non-Rayleigh distributed matched filter envelope in literature. Conventional constant false alarm rate (CFAR) algorithms fail to achieve a constant false alarm rate in all ranges and bearing in the presence of such effects. Given sufficient information on the bathymetry and the bottom properties, the occurrence of FARI may be estimated through the use of an acoustic model. This allows for more accurate estimates of the false alarm rate. Through measurements, the scatterer statistics for a given sonar in a given area may be estimated. Combining a FARI predicting scheme with knowledge on the scatterer statistics allows the estimation of the probability of false alarm in the presence of both FARI and sonar clutter. Here, we propose a new detection scheme that employs this approach to estimate a range- and bearing-dependent threshold that can be applied on normalized sonar data to achieve a CFAR even in the presence of FARI and clutter. The performance of the method is assessed through the use of receiver operating characteristic curves and is shown to outperform conventional CFAR algorithms, such as the cell averaging, greater of, and ordered statistics CFAR processors. The method is tested on both recorded and synthetic data. The robustness of the method is tested using synthetic data by introducing errors in the topography, sound speed, and scatterer statistics when estimating the probability of false alarm. The performance of the method decreases when introducing these errors, but it still outperforms the conventional CFAR processors.
Data Processing Method of Multibeam Bathymetry Based on Sparse Weighted LS-SVM Machine Algorithm
X. Huang, C. Huang, G. Zhai, X. Lu, G. Xiao, L. Sui, and K. Deng
In this paper, on the basis of the sparse weighted least-squares support vector machine (LS-SVM) algorithm, the sparse weighted LS-SVM surface is established and the corresponding steps are given. Then, multibeam bathymetric anomalies can be detected using the sparse weighted LS-SVM surface. The construction of the sparse weighted LS-SVM surface requires four aspects: selection of kernel function, parameters calculation of sparse weighted LS-SVM, selection of support vectors, calculation of weight coefficients. Finally, to verify the validity of sparse weighted LS-SVM surface in multibeam bathymetric anomalies detection, the measured multibeam bathymetric data are selected to calculate and analyze. The conclusion of the experiment is introduced, that is, the sparse weighted LS-SVM surface has a better performance compared to the traditional polynomial surface function, and the sparse weighted LS-SVM surface is more capable of reflecting the overall change of seabed topography. Compared with the combined uncertainty and bathymetry estimator surface, the proposed method utilizes the raw soundings rather than the nodes, so the sparse weighted LS-SVM surface can better reflect the detailed information of seabed topography.
Improving Swath Sonar Water Column Imagery and Bathymetry With Adaptive Beamforming
T. I. Birkenes Lønmo, A. Austeng, and R. E. Hansen
Modern swath sonar is a mature technology today and has reached a very high level of sophistication including techniques to increase area coverage rate, data quality, and resolution. There is, however, often a need to explore features at the limit of what is resolvable. It is, therefore, of interest to consider alternative signal processing techniques for a given physical system. For the traditional delay-and-sum (DAS) beamformer there is a tradeoff between angular resolution and sidelobe suppression. Using either the Capon or the low complexity adaptive (LCA) beamformer, the water column edge definition, sidelobe level, and resolution are improved compared to a moderately weighted DAS beamformer. These improvements are similar to the recent results for sector-scanning sonar. This leads to improved performance for the amplitude-based center-of-gravity bottom detector. The Capon beamformer performs best, while the LCA beamformer has a large part of the improvement with higher robustness and easier implementation. We use simulations to show the improvements of the adaptive beamformers from nadir up to 42o across track, and validate the results using field data. The improvements in the water column increase the separation between features and noise, and collapse the apparent size of features down to more realistic dimensions. These improvements allow the adaptive beamformers to reveal features that are hidden for the DAS beamformer.
Performance of a Passive Acoustic Linear Array in a Tidal Channel
M. F. Auvinen, D. R. Barclay, and M. E. W. Coffin
Baseline ambient sound level assessment is important in quantifying anthropogenic noise contributions. Static acoustical sensing in high-flow conditions is complicated by pseudosound, or flow noise, caused by turbulent flow on the surface of a hydrophone. Signal processing methods are used to identify and suppress flow noise at low frequencies (<500 Hz) in data collected on a four-element horizontal hydrophone array in Minas Passage, a tidal channel in the Bay of Fundy, in October 2016. Spectral slope analysis is used to identify the frequencies at which the flow noise and ambient noise contributions to the recorded signal are equal, between 180 and 275 Hz for current speeds of 0.1 to 2.6 m/s, respectively. The maximum frequencies at which flow noise is observed, between 100 and 125 Hz over the same current speed range, are determined using analysis of the spatial coherence. The array's performance in the Minas Passage is quantified by an empirical relationship between flow speed and the spectral critical frequencies of the coherent output from the linear array. Coherent averaging (broadside beamforming) is demonstrated as a potential flow noise suppression technique, improving low-frequency passive acoustic monitoring in a high-energy tidal channel.
Closed-Form Estimation of Normal Modes From a Partially Sampled Water Column
H. Gazzah and S. M. Jesus
The output of a vertical linear array is used to infer about the parameters of the normal mode model that describes acoustic propagation in a shallow water. Existing subspace algorithms perform singular vector decomposition of the array data matrix to estimate the sampled model functions. Estimates are exact only if the sensing array is totally covering the water column. We design a new subspace algorithm free from this very restrictive requirement. We use two short hydrophone arrays and activate a monochromatic source at different depths. Estimates of both the modal functions and the wave numbers are obtained in a fully automatic and search-free manner. The algorithm can be qualified as truly high resolution in the sense that, while using short sensing arrays, estimation error becomes arbitrarily low if observation noise is arbitrarily low. This method compares advantageously to existing subspace techniques, as well as transform-domain techniques that require impulsive sources, among other constraints. With two (eigen and singular) vector decompositions, the proposed technique has the complexity of a regular subspace algorithm.
Matched Field Processing in Phase Space
A. L. Virovlyansky, A. Yu. Kazarova, and L. Ya. Lyubavin
The traditional method of matched field processing is based on comparing the complex amplitudes of the measured and calculated sound fields in an underwater waveguide. Because of the high sensitivity of the wave field to variations in environmental parameters, the use of this approach requires accurate knowledge of the ocean-acoustic environment. In this paper, it is shown that under conditions of uncertain environment, instead of comparing the depth dependencies of complex field amplitudes, it is advisable to compare their field amplitude distributions in the phase plane "grazing angle-depth." Such distributions are calculated using the coherent state expansion borrowed from quantum mechanics. Due to the absence of multipath in the phase space, the amplitudes of the coherent states are less sensitive to variations in the environmental parameters than the total wave field. This makes it possible to construct the similarity coefficients of measured and calculated fields that almost "do not notice" the differences of the compared fields caused by weak sound-speed variations, and "react" only to differences caused by strong changes in the sound-speed field and/or source position.
Underwater Acoustic Communication Using Multiple-Input–Multiple-Output Doppler-Resilient Orthogonal Signal Division Multiplexing
T. Ebihara, H. Ogasawara, and G. Leus
In this paper, we propose a novel underwater acoustic (UWA) communication scheme that achieves energy and spectrum efficiency simultaneously by combining Doppler-resilient orthogonal signal division multiplexing (D-OSDM) and multiple-input-multiple-output (MIMO) signaling. We present both the transmitter processing and the receiver processing for MIMO D-OSDM. We evaluate the performance of MIMO D-OSDM in simulations with a large intersymbol interference of 25 symbols and a Doppler spread with a maximum Doppler shift of 8 Hz. In addition, the sea trial is performed in Suruga Bay, where the receiver is mounted on a barge and a research vessel with the transmitter makes round trips along a line with a speed of 4 kn. In the experiments, we obtain an intersymbol interference of 3.6-29.7 symbols and a Doppler spread of several Hertz (leading to a spread of over two to three subcarrier spacings). The simulation results suggest that MIMO D-OSDM has an advantage over normal D-OSDM, Doppler-resilient MIMO orthogonal frequency division multiplexing (MIMO D-OFDM), and classical OFDM with MIMO signaling (MIMO OFDM)-MIMO D-OSDM achieves better bit error rate performance than the benchmarks. The sea trial results also support the advantage of MIMO D-OSDM-it achieves a coded block error rate of 3.2% while normal D-OSDM and MIMO D-OFDM achieve a coded block error rate of 9.7% and 9.3%, respectively. We conclude that MIMO D-OSDM can become a viable technique that achieves reliable and effective UWA communication.
Silicon-Photomultiplier-Based Underwater Wireless Optical Communication Using Pulse-Amplitude Modulation
M. A. Khalighi, H. Akhouayri, and S. Hranilovic
Emerging maritime applications arising from the continued growth of the marine economy have an inherent need for high data rate underwater wireless links. Within this context, underwater wireless optical communication is known as a promising technology for data transmission over short-to-medium ranges; the current available technology provides a transmission span of about 100 m. In view of extending the transmission range, silicon photomultipliers (SiPMs) have recently emerged as a photodetection solution offering high receiver (Rx) sensitivity together with operational flexibility. In this paper, we introduce the use of pulse-amplitude modulation (PAM) together with frequency-domain equalization (FDE) at the Rx to boost the communication rate beyond the bandwidth (BW) limitation of the optoelectronic components. For instance, for a link BW limited to 2 MHz and 2-PAM transmission with a target bit-error rate (BER) of 10-4, the link becomes nonoperational for data rates higher than ~8 Mbps without equalization, whereas much higher data rates can be attained using FDE, e.g., 20 and 50 Mbps with maximum ranges of 28 and 10 m, respectively, in clear waters for the SensL MicroSB30020 SiPM and an average transmit optical power of 0.6 W only. Meanwhile, the nonlinear distortion of the SiPM is shown to limit the modulation order and thus the data rate in relatively short ranges. We also propose appropriate processing for PAM modulation and demodulation, given the quantum-noise-limited Rx when using an SiPM. We show that the use of nonbinary PAM is undeniably advantageous for moderate data rates (symbol rate a few MHz higher than the overall link BW) when no channel equalization is performed at the Rx. However, when employing FDE, only for very high data rates (e.g., symbol rate ten times higher than the link BW), where the link frequency response becomes highly frequency selective, the nonbinary PAM becomes practically interesting, outperforming 2-PAM.
Sparse Direct Adaptive Equalization for Single-Carrier MIMO Underwater Acoustic Communications
J. Tao, Y. Wu, X. Han, and K. Pelekanakis
The sparse direct adaptive equalization technique recently received many attentions for single-carrier underwater acoustic communications. By taking advantage of the sparse (nonuniform) structure of a direct adaptive equalizer (DAE), one obtains a sparse DAE with improved performance and/or reduced complexity compared with its nonsparse counterpart. In this article, the sparse DAE is revisited with two contributions made: First, a comprehensive comparison is made for existing sparse DAEs designed with the proportionate-updating (PU) or the zero-attracting (ZA) adaptive filtering principles. The comparison concludes that the PU-type DAE outperforms the ZA-type DAE in terms of complexity, performance, and operability, thus shall be favored in practical use. Moreover, it motivates the design of a sparse DAE, named the selective ZA improved proportionate normalized least mean squares DAE (SZA-IPNLMS-DAE), based on the combination of the PU and ZA principles. The SZA-IPNLMS-DAE outperforms existing sparse DAEs armed with only one sparsity-promoting mechanism; second, a partial tap update (PTU) scheme via hard thresholding is introduced to sparse DAEs for reducing their complexity without sacrificing performance. The resulting low-complexity and high-performance sparse PTU-DAE schemes are strong candidates for single-carrier UWA communications. Experimental results of multiple-input multiple-output UWA communications are presented to corroborate all above claims.
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