Sonar Array Research Articles

Sparsity-based direction-of-arrival estimation in the presence of near-field and far-field interferences for small-scale platform sonar arrays.

For the sonar arrays mounted on an unmanned underwater vehicle (UUV), the direction-of-arrival (DOA) estimation of the far-field (FF) weak sources is influenced by the near-field (NF) interferences generated from the radiated self-noise of the UUV and the FF interferences simultaneously. To address the problem, a sparsity-based DOA estimation method resistant to the NF and FF interferences is proposed in this paper. This method isolates the FF signals from the NF signals by sparse reconstruction. Additionally, subspace projection is applied to address the masking problem of the weak target signal by the strong interferences in the spatial domain, effectively enhancing the capacity of estimating the DOA of the weak target signal in the presence of strong interferences. Numerical simulations and experimental results demonstrate the effectiveness of the proposed method. Compared to other advanced DOA estimation methods, the proposed method exhibits better DOA estimation performance in the presence of strong NF and FF interferences.

Enhancing SONAR performance through statistically-informed sub-array processing

Ocean fluctuations affect acoustic propagation and reduce the performance of SONAR arrays. In particular, internal waves can lead to a loss of coherence in incident signals, reducing the efficiency of classical processing. Precise knowledge of the environment is necessary to predict the degradation it causes. However, it is impossible to know the exact state of the ocean, it is therefore necessary to use statistical methods to study the ocean and acoustic propagation. In this work, statistical method, canonical correlation analysis (CCA), is used to find linear relationships between acoustic variables of interest and oceanographic measurements. In particular, the coherence radius is inferred from empirical modes of temperature using the model learned by CCA. A sub-array processing scheme can then be informed by defining the length of the sub-arrays from the coherence radius. To compensate for the loss of angular resolution inherent in sub-arrays, we introduce the use of a Bayesian algorithm exploiting l0 norm regularization. We show on experimental data from the ALMA 2017 campaign that the proposed processing improves detection when signal coherence is low.

Multipath Track Association Technique using Characteristics of Conical Bearing in Horizontal Line Array Sonar

This paper conducts a study on methods for associating multipath tracks received by a horizontal line array sonar. The horizontal line array sonar introduces conical bearing ambiguities due to its array-shaped characteristics. Target signals are often subject to multipath propagation in the ocean, resulting in errors in target bearing estimation due to these bearing ambiguities. Recent research efforts have been directed toward using multipath tracks to estimate the positions of targets. However, there has been a lack of research dedicated to identifying multipath tracks from the measurements of a horizontal line array sonar. Therefore, this study identifies the conical bearing characteristics of multipath signals based on the result of experiment and establishes criteria for associating multipath tracks. Additionally, Simulations are performed to determine the ability to associate multipath tracks.

Three-dimensional tracking of a transmitter under conic angle-frequency measurements of linear array sonars

This study considers tracking an underwater transmitter using a linear array sonar. The linear array sonar measures both the transmitter’s frequency and the conic angle with respect to the array’s direction. By analyzing the transmitter’s conic angle and frequency, the array sonar sensor estimates the transmitter’s three-dimensional position and (constant) velocity. This tracking problem is termed the conic angle-frequency tracking (CAFT) problem in our paper. We first consider tracking a transmitter which moves with a constant velocity. Then, we extend the tracking filter, so that one can track a transmitter which changes its heading occasionally. For tracking a transmitter which changes its heading in three-dimensional space, we propose a filter restart scheme. As the transmitter’s heading change is observed, one restarts all filter tracks so that the transmitter can be tracked continuously after its abrupt heading change. To the best of our knowledge, this study is unique in solving the three-dimensional CAFT problem for a constant velocity transmitter or a transmitter which changes its heading occasionally. The effectiveness of the proposed three-dimensional CAFT filter is demonstrated utilizing computer simulations.

Transforming discrete movements of a sonar array into smooth vibration of a plate: Constant force input versus constant displacement input

Numerical modeling of systems is an efficient way to explore new ideas, but an understanding of modeling assumptions as they relate to the actual physics of the system is critical. As a graduate student, I inherited a potentially revolutionary project involving sonar arrays. The idea was that grating lobes in the sound radiation from arrays could be eliminated by placing a plate between the array and the water. Grating lobes result from unintended, aliased superposition of traveling waves when discrete transducers attempt to create a single traveling wave in the array’s plane. The model initially assumed the transducers provided a constant displacement input to the plate’s vibration and the plate, thus, transformed the discrete traveling wave into a smooth continuous wave, resulting in elimination of grating lobes. The problem was that the plate’s impedance was higher than the transducers’ impedance and, thus, a constant force input was a better approximation to use in the model. When accurate assumptions were used, the grating lobes returned. A new idea was conceived to make something useful out of the original idea, though it required yet undeveloped materials. Although unexpected and disappointing at the time, “negative discoveries” can sometimes lead to positive ones.

Motion tracking of fish and bubble clouds in synthetic aperture sonar data.

Data captured by a Synthetic Aperture Sonar (SAS) near Mobile Bay during the 2021 Undersea Remote Sensing experiment funded by the Office of Naval Research reveals near surface bubble clouds from wave breaking events and a large aggregation of fish. Tools developed for using SAS data to image hydrodynamic features in the water column were applied to observations of the bubble clouds and fish aggregation. Combining imagery and height data captured by the sonar array with a detection and tracking algorithm enables the trajectories, velocities, and behavior of fish in the aggregation to be observed. Fitting the velocity and height data of the tracked objects to a Gaussian mixture model and performing cluster analysis enables an estimate of the near-surface ambient velocity via observation of the movement of the bubble traces and the general direction of motion of the fish aggregation. We find that the velocity traces associated with bubbles are consistent with ambient currents as opposed to the direction of propagating wave crests while velocities of fish indicate relatively large, pelagic species.

A DOA Estimation Method Based on an Improved Transformer Model for Uniform Linear Arrays with Low SNR

In this paper, the Star-Transformer model is improved to obtain more accurate direction of arrivals (DOA) estimation of underwater sonar uniform linear array (ULA) under low signal-to-noise ratio (SNR) conditions. The ideal real covariance matrix is divided into three channels: real part channel, imaginary part channel, and phase channel to obtain more input features. In training, the real covariance matrix is used under different SNRs. In testing, the covariance matrix of samples in the real environment is used as input. The on-grid form is used to estimate the DOA of multiple signal sources, which is modelled as a multilabel classification problem. The results show that the model can be effective and can still have a good DOA estimation performance under the conditions of trained and untrained SNRs, different snapshots, signal power mismatch, different separation angles, signal correlation, and so on. It shows that the model has excellent robustness.

Research on a Feature Enhancement Extraction Method for Underwater Targets Based on Deep Autoencoder Networks

The low-frequency line spectrum of the radiated noise signals of hydroacoustic targets contains features describing the intrinsic properties of the target that make the target susceptible to exposure. In order to extract the line spectral features of underwater acoustic targets, a method combining image processing and a deep autoencoder network (DAE) is proposed in this paper to enhance the low-frequency weak line spectrum of underwater targets in an extremely low signal-to-noise ratio environment based on the measured data of large underwater vehicles. A Gauss–Bernoulli restricted Boltzmann machine (G–BRBM) for real-value signal processing was designed and programmed by introducing a greedy algorithm. On this basis, the encoding and decoding mechanism of the DAE network was used to eliminate interference from environmental noise. The weak line spectrum features were effectively enhanced and extracted under an extremely low signal-to-noise ratio of 10–300 Hz, after which the reconstruction results of the line spectrum features were obtained. Data from large underwater vehicles detected by far-field sonar arrays were processed and the results show that the method proposed in this paper was able to adaptively enhance the line spectrum in a data-driven manner. The DAE method was able to achieve more than double the extractable line spectral density in the frequency band of 10–300 Hz. Compared with the traditional feature enhancement extraction method, the DAE method has certain advantages for the extraction of weak line spectra.

Influence of bioinspired riblet topographies on the mitigation of flow-induced noise in towed sonar arrays

The hydrodynamic optimization of marine vehicles and devices is a critical area of research, especially regarding the diminution of flow-induced drag pertinent to towed sonar arrays. The utilization of bio-inspired riblets, which emulate the skin of rapid marine predators such as sharks, has been posited as a viable strategy to mitigate hydrodynamic drag. Nonetheless, the implications of these riblets on the concurrent acoustical emissions during flow-array interaction remain not well understood. This study conducts an in-depth investigation into the performance of bioinspired riblet topologies in attenuating flow-induced noise in towed sonar arrays, scrutinizing their effectiveness across laminar and turbulent flow regimes. In laminar flow regimes, the study observes that riblet structures have the potential to reduce noise but at the cost of increased drag for towed arrays. Conversely, in turbulent flow conditions, the effectiveness of riblets is significantly enhanced. Particularly, rectangular riblets demonstrate a notable reduction in noise by up to 14.3%, along with a 5.1% decrease in hydrodynamic drag, in comparison to a smooth array surface. Additionally, our parametric study reveals that riblets with finer, more closely spaced geometries can further reduce the drag by an additional 25.7%. These findings underscore the efficacy of riblet geometries in different flow conditions, highlighting their potential in optimizing the acoustic and hydrodynamic performance of maritime systems.

Non-Invasive Plant Root Tomography Through Optimized Sonar Array Transducer Antenna Design Using Genetic Swarm Metaheuristic

Plant root imaging is crucial for progress in various domains such as plant breeding and crop optimization. Traditionally, root tomography involves invasive methods that disrupt plant systems and yield non-reproducible results. As a result, non-invasive techniques, particularly electrical tomography, have gained significant attention. Despite the advantages, these techniques have limitations in terms of radiation efficiency and directivity due to suboptimal antenna design. This paper presents a comprehensive simulation on antenna design optimization focusing on dimensions, spacing, and integration of advanced algorithms. A micropatch transducer antenna was engineered for an existing in-silico plant root setup operating within a 3–5 MHz frequency range. The optimized dimensions of the antenna are 109.32 mm × 140.67 mm × 2.55 mm, and it resonates effectively within a frequency range of 3.1–5.68 MHz. Using scalar minimization techniques, patch transducers were interconnected into an antenna array with an optimized 3 mm spacing. Utilizing multi-objective optimization algorithm based on sperm fertilization procedure and shuffled frog leaping algorithm, optimal frequencies were obtained at 3,989,796.88 Hz and 3,989,951.83 Hz, respectively. Validated using CADFEKO software, the proposed antenna design demonstrated distinctive voltage distribution, superior directivity of 9.24 dBi, gain of 9.15 dBi, and 98.6% radiation efficiency when compared to the existing silicon-based root tomography antenna setups.

Quantum-enhanced optical phase tracking via squeezed state

Quantum-enhanced optical phase tracking is a quantum optical technique for tracking and measuring optical phases with high accuracy. It has important applications in laser interferometry, spectral analysis, and optical measurements. In this study, we propose a quantum-enhanced optical phase tracking protocol based on squeezed state optical fields. By using a continuous solid-state laser source with a central wavelength of 1064 nm, combing second harmonic generation, optical parametric oscillator, and PDH (Pound-Drever-Hall) locking technology, we prepare an initial squeezed state with a squeezing level of (8.0±0.2) dB. Through signal modulation technique and demodulation technique, we control the phase of the squeezed state optical field, thereby realizing the quantum-enhanced tracking of optical phases within the range of 0－2π. Compared with classical protocols, this protocol can suppress the noise fluctuations of phase tracking to at least 6.27 dB below the shot noise limit, improving the phase tracking accuracy by more than 76.4%. Because of the high requirements for phase measurement accuracy in applications such as angle estimation, phased array radar, and phased array sonar, this protocol is expected to improve the phase estimation accuracy beyond the shot noise limit. It provides compressed light sources for relevant fields, laying a theoretical and experimental foundation for higher-precision spatial positioning and quantum ranging techniques. The probe is made of amino acids arranged in a linear chain and joined together by peptide bonds between the carboxyl and amino groups of adjacent amino acid residues. The sequence of amino acids in a protein is determined by a gene and encoded in the genetic code. This can happen either before the protein is used in the cell, or as part of control mechanism.

Surface Target Ship Localization Utilizing Multiple Line Array Sonars

Surface Target Ship Localization Utilizing Multiple Line Array Sonars

Research on Characteristics of Flow Noise and Flow-Induced Noise

The noise control of flank array sonar is a primary approach to enhance the sonar detection range. During submarine navigation, hydrodynamic noise is the main noise source in the platform region of the flank array sonar, which includes flow noise and flow-induced noise. Therefore, an in-depth investigation of hydrodynamic noise is necessary. In this paper, we firstly take the teardrop submarine as a computational model to validate the computational method. Afterwards, we numerically simulate the flow and flow-induced noise characteristics for the cylindrical shell model, and investigate differences in noise at different detection points along the X, Y, and Z axes. Finally, experiments are conducted to confirm the accuracy of the simulation results. The research findings reveal that, at the same frequency, flow-induced noise exceeds flow noise, and the noise decreases as the distance between the walls of the cylindrical shell increases. The experimental and simulation results are consistent, suggesting that the selected computational method can precisely simulate the submarine’s noise.

Source localization in deep water with a towed array from UUV

Unmanned Underwater Vehicle (UUV) can work in larger depth. If a towed array applied on UUV, it can detect the acoustics signals from a target in the direct arrival zone with lower transmission loss in deep water. Based on sound field characteristics in deep water, a source detection scheme with multiple UUVs is proposed. The aperture of sonar array is limited by the carrying capacity of UUV. Therefore, a passive aperture extension with sparse Bayesian learning (SBL) is proposed for a moving short array to improve the performance of direction-of-arrival estimation by constructing a larger virtual aperture. The phase correction factors and bearings of different targets are estimated simultaneously by SBL. The experiment results show that the method extends the aperture effectively and obtains higher azimuth resolution and accuracy for the direction-of-arrival (DOA) estimation than the Conventional Beamforming (CBF). The detection of an underwater target by using a towed array from UUV is possible. [Work supported by the National Natural Science Foundation of China, Grant No. U22A2012.]

An augmented state Gaussian mixture probability hypothesis density filter for multitarget tracking of autonomous underwater vehicles

Multitarget tracking is an important task for autonomous underwater vehicles (AUVs). Due to the limited space on AUVs, the aperture of the sonar array mounted on an AUV is small. The finite resolution of AUV sonar systems results in weak targets being masked by strong targets when they are close in angular space, leading to the trajectory fracture problem of weak targets in the azimuth crossing scenario. To address this problem, an augmented state Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed. Since the acoustic signals radiated by targets contain some narrowband discrete components (known simply as tonals or lines), we focus on leveraging the tonals to enhance tracking performance. First, we model the augmented state dynamics, which contain both direction-of-arrival (DOA) and tonal components, and DOA tracks can be generated by the augmented state GM-PHD filter. Second, track segments are clustered by the modified density-based spatial clustering of applications with noise (DBSCAN) algorithm according to the estimated tonals. Finally, track segments with the same clustering results are associated with the same target, and the track stitching method is applied to overcome the trajectory fracture problem. The effectiveness of the algorithm is verified by simulation experiments in different scenarios.

Effects of internal solitary waves on three-dimensional sound propagation and DOA estimation in the South China Sea

Internal solitary waves (ISWs) in the South China Sea, characterized by their large amplitude and strong nonlinearity, have attracted a lot of attention. In this paper, we use the Massachusetts Institute of Technology general circulation model (MITgcm) to construct the processes of generating and propagating ISWs in the South China Sea from the Luzon Strait to the continental shelf. The simulated east–west flow velocity is compared with remote sensing images, and the results demonstrate the accuracy of the ISW model in the South China Sea. Inserting the simulated three-dimensional (3D) sound speed from the ISW model into the Bellhop3D model, key findings emerge that ISWs cause depressions in the iso-sonic surface, which lead to drastic fluctuations in sound rays and the horizontal refraction effect in the progress of sound propagation. ISWs disturb the convergence zone of sound rays, but the position of the internal wave crest line is consistent with that of the collective deflection of rays. In addition, the results of direction of arrival (DOA) estimation under ISWs demonstrate that the internal wave increases the DOA estimation error. Together, ISWs induce fluctuations in sound propagation and degrade sonar performance. This study creatively combines ISWs with the underwater sound field and DOA estimation, which provides support for the 3D sound propagation mode and the arrangement of the sonar array in the internal wave environment.

Spatial focusing algorithm for source enumeration with rotating uniform circular sonar arrays

AbstractSource enumeration with large snapshots is feasible for a static uniform circular sonar array (UCSA). However, the dimension of the signal subspace in the array coordinate system exceeds the number of signal sources due to the rotation of the UCSA, which makes standard source enumeration ineffective. To address this issue, a spatial focusing approach for source enumeration with rotating UCSAs is presented. The approach is based on the concept of phase‐mode excitation and makes use of the UCSA heading information to achieve spatial focusing. The proposed algorithm provides reliable source enumeration with a high signal‐to‐noise ratio (SNR) when compared to the previous signal‐subspace transformation (SST) method. Simulations demonstrate that the proposed algorithm outperforms the other three methods in the conditions of a low SNR and a small angular separation.

Corrigendum: Fast estimation of direction of arrival based on sparse Bayesian learning for towed array sonar during manoeuvring

Corrigendum: Fast estimation of direction of arrival based on sparse Bayesian learning for towed array sonar during manoeuvring

Three-dimensional observations of tidal plume fronts in estuaries using a synthetic aperture sonar array.

Synthetic aperture sonar (sas) systems are designed to observe stationary scatterers located near the sediment interface. Less commonly, a sas system may be used to observe scattering features located above the sonar in the water column. The Undersea Remote Sensing (USRS) project, sponsored by the Office of Naval Research, was a collaborative Directed Research Initiative (DRI) focused on studying dynamic estuarine water column features. During the USRS DRI, researchers from multiple institutions gathered to observe tidal features at various estuaries along the coast of the United States using both in situ and remote sensing techniques, including sas. The first studied estuary was the mouth of the Connecticut River (CTR). Data captured by a sas system deployed during a tidal event were post-processed to create three-dimensional observations of the structure of the leading edge of the CTR's ebb plume front. From these observations, lobed structures similar in scale to previously reported instabilities are revealed, with the present observations providing additional insight regarding the structure of the bubble distribution behind the front. Velocity estimates of plume features were also determined from sas data and shown to compare favorably with concurrent marine radar estimates.

Self-Interference Suppression of Unmanned Underwater Vehicle with Vector Hydrophone Array Based on an Improved Autoencoder

The self-interference of an unmanned underwater vehicle (UUV) weakens its ability to detect targets of interest. Due to limitations in the size of the sonar array and the complexity of the interference, the performance of existing self-interference suppression methods in practical applications is unsatisfactory. Our research focuses on analyzing the influence of near-field interferences on the sample covariance matrix (SCM) and proposes an interference suppression algorithm based on an improved autoencoder. The proposed algorithm effectively learns the feature distribution of near-field interferences within the covariance domain and reconstructs the pure signal covariance matrix through the cancellation of the near-field interference features. Moreover, the proposed algorithm can meet the requirements of real-time processing and does not require prior knowledge about the positions or propagation of interference. Simulations demonstrate that the proposed algorithm outperforms comparison methods, particularly in scenarios with low signal-to-interference ratios and a limited number of sensors. Furthermore, lake experiments provide additional evidence of the proposed algorithm’s good performance in practical applications.