Sonar Array Research Articles

Near range breakdown of the phase centre approximation

AbstractSynthetic aperture sonar image reconstruction relies on the coherence of overlapping phase centres to provide accurate micronavigation for a sensed scene. It is shown that phase centres lose coherence for near‐range scattering from large synthetic aperture sonar arrays due to the fundamentally bistatic nature of these sensors. This effect is modelled using the van Cittert‐Zernike theorem and a point‐based sonar scattering model. Reduction of the window length used in the delay estimation process can partially mitigate the loss of coherence at the expense of increased variance in the resulting delay estimates.

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

AbstractFor improving performance of the towed array sonar during the manoeuvring period, a processing framework is proposed based on joint estimation of direction of arrival (DOA) and the array shape. The sparse Bayesian learning (SBL) algorithm is utilised to accurately estimate the DOAs of interesting targets. Meanwhile, the towed array shape is modelled as a parabola array, and the array element positions are estimated using the bow of the parabola. Then, an approximate posterior covariance is implemented to the SBL for a fast converging speed required by practical applications. Besides, the co‐prime array structure is considered for the uniform linear array to further reduce the computational load. The numerical simulations have verified the effectiveness of the fast converging SBL method. MAPEX2000 experimental data processing results have shown that the proposed framework performs well in detection of weak targets during slow turns.

A Wide-Area Deep Ocean Floor Mapping System: Design and Sea Tests

Mapping the seafloor in the deep ocean is currently performed using sonar systems on surface vessels (low-resolution maps) or undersea vessels (high-resolution maps). Surface-based mapping can cover a much wider search area and is not burdened by the complex logistics required for deploying undersea vessels. However, practical size constraints for a towbody or hull-mounted sonar array result in limits in beamforming and imaging resolution. For cost-effective high-resolution mapping of the deep ocean floor from the surface, a mobile wide-aperture sparse array with subarrays distributed across multiple autonomous surface vessels (ASVs) has been designed. Such a system could enable a surface-based sensor to cover a wide area while achieving high-resolution bathymetry, with resolution cells on the order of 1 m2 at a 6 km depth. For coherent 3D imaging, such a system must dynamically track the precise relative position of each boat’s sonar subarray through ocean-induced motions, estimate water column and bottom reflection properties, and mitigate interference from the array sidelobes. Sea testing of this core sparse acoustic array technology has been conducted, and planning is underway for relative navigation testing with ASVs capable of hosting an acoustic subarray.

Mitigating, monitoring and assessing the effects of anthropogenic sound on beaked whales

Certain anthropogenic sounds are widely believed to cause strandings of beaked whales, but their impacts on beaked whale populations are not known and methods for mitigating their effects are largely untested. The sound sources that have been coincident with beaked whale strandings are military, mid-frequency sonar (2-10kHz) and airgun arrays, both of which are used widely throughout the world for defence and geophysical exploration, respectively and for which alternative technologies are not readily available. Avoidance of beaked whale habitats is superficially a straightforward means of reducing the potential effects, but beaked whales are widely distributed and can be found in virtually all deep-water marine habitats that are free of ice. Some areas of high beaked whale abundance have been identified, but the geographic distribution is poorly known for most species. Beaked whales are both visually and acoustically difficult to detect. Commonly used mitigation measures (e.g. ‘ramp-up’ and ‘detection-modification-avoidance’) have not been assessed for their effectiveness. Surveys to detect population-level impacts would likely require many years of regular monitoring and for most areas where beaked whale strandings have occurred, there are no pre-exposure estimates of population sizes. Risk assessment models can be used to estimate the sound levels to which beaked whales might be exposed under a variety of scenarios, however, the lack of information on the causal mechanism for soundrelated beaked whale strandings makes it difficult to identify exposure levels that would warrant mitigative actions. Controlled exposure experiments, which measure the behavioural responses of animals to fully characterised sound sources, may hold the greatest potential for understanding the behavioural responses of beaked whales to sound and for designing mitigation methods to avoid future impacts.

Array invariant-based range-only target motion analysis for ship noise measured by two vertical line arrays in shallow water

A novel approach for underwater acoustic localization by using passive line array sonar will be proposed in this talk. In the case of the received signal having a finite pulse duration, the target location can be easily estimated using the time-difference-of-arrival (TDOA) method between sensors. However, it is difficult to use the TDOA method for signals that exist for a relatively long time, such as ship noise. An array invariant method has been proposed to measure the source-receiver distance, but it does not accurately estimate the target location. In this talk, target localization will be performed with the range-only target motion analysis method using non-linear target tracking after estimating the target range by the array invariant method. The proposed method is tested using the ship noise of the R/V Onnuri measured by two vertical line arrays during the Shallow-water Acoustic Variability Experiment (SAVEX-15), and the result will be discussed in this talk. [This research was supported by the Ministry of Oceans and Fisheries of Korea (PM63010, 2022).]

Broadband beamforming of multiplet line arrays using subband optimal beamformers eliminating port/starboard ambiguity

Development of beamforming methods for towed sonar multiplet line arrays is stimulated by their increased use in undersea surveillance. Such methods employ various forms of narrowband optimal beamforming to resolve port/starboard ambiguity present when the array is conventionally beamformed. In this paper, we depart from the purely narrowband approach and discuss methods for port/starboard ambiguity rejection (PSAR) using a broadband formulation. The proposed methodology relies on subband beamforming for which we use non-adaptive variants of beamspace Minimum Variance Distortionless Response (MVDR) and the Linear Constraint Minimum Variance (LCMV) beamformers. We provide detailed description and comparison of both subband methods and establish a connection between them. The paper provides assessment of PSAR properties of the developed broadband beamformers using outputs of signal processing of both simulated and experimental sonar data. Experimental data for this work were obtained through the participation in the Littoral Continuous Active Sonar (LCAS) trials.

Noise Suppression of Towed Line Array Sonar Platform Based on Spatial Filtering Technique

The problem of interference caused by the radiated noise of the towed line array sonar platform to the hydrophone line array and the formation of detection blind area in the direction of the end head of the line array is addressed. In this paper, the optimal spatial matrix filter is designed using the plane wave array manifold, the platform radiation noise copy vector, and the weighted coefficient matrix. Through iteration of the weighted coefficient matrix, the null matrix filter achieves the effect of equalizing the response of each direction of the far-field plane wave signal under the condition of zero response constraint of the platform radiation noise. The method of generalized singular value simplified decomposition is used to analyze and check the correctness of the optimal solution of the filter in theory. From the simulation results, the filter with iterative calculation is more effective in processing far-field plane waves and the signal has lower distortion.

Deblurring of Sound Source Orientation Recognition Based on Deep Neural Network.

Underwater target detection and identification technology are currently two of the most important research directions in the information disciplines. Traditionally, underwater target detection technology has struggled to meet the needs of current engineering. However, due to the large manifold error of the underwater sonar array and the complexity of ensuring long-term signal stability, traditional high-resolution array signal processing methods are not ideal for practical underwater applications. In conventional beamforming methods, when the signal-to-noise ratio is lower than −43.05 dB, the general direction can only be vaguely identified in the general direction. To address the above challenges, this paper proposes a beamforming method based on a deep neural network. Through preprocessing, the space-time domain of the target sound signal is converted into two-dimensional data in the angle-time domain. Subsequently, we trained the network with enough sample datasets. Finally, high-resolution recognition and prediction of two-dimensional images are realized. The results of the test dataset in this paper demonstrate the effectiveness of the proposed method, with a minimum signal-to-noise ratio of −48 dB.

Lisa Zurk and Matched Field Processing—Her early career at MIT Lincoln Laboratory (1996–2005)

After completing her doctoral program in electromagnetics in 1995 Lisa decided to go to MIT Lincoln Laboratory to work on Matched Field Processing (MFP) in a nascent group formed for sonar and underwater acousitcs efforts. Then MFP introduced new methods of beamforming which were found to be sensitive to assumptions made for the sonar system, the environment and array processing. The so called adaptive one were especially sensitive. Lisa examined the importance of source motions and array are important for any sonar. In this context she introduced (i) mode based rank reduction, or modal MFP, (ii) array tile corrections, (iii) a “dynamic” snapshot correction method a focused MFP while the source is moving similar to beam steered MVDR, and (iv) “instantaneous” data and model based interference filtering. All these were subsequently discovered by later MFP researchers. Lisa and her coauthors were well ahead of there time.

Focusing on motion and averaging the diagonal: Lisa Zurk’s many contributions to passive sonar array processing

Lisa Zurk’s numerous contributions to passive sonar signal processing include motion compensation and regularizing sample covariance matrices (SCM) in snapshot deficient environments. While working at MIT Lincoln Labs, Lisa and colleagues compared the performance of several motion compensation methods including mode-based rank reduction and target motion compensation, or focusing, on the Santa Barbara Channel Experiment dataset [Zurk, Lee & Ward, JASA, 2003]. Later at Portland State, Jorge Quijano and Lisa developed methods for regularizing the SCM through Toeplitz averaging, followed by maximum entropy extrapolation of the SCM to additional lags, [Quijano & Zurk, JASA, 2017]. Toeplitz regularization found significant application in developing “augmented covariance matrices” for DOA estimators on sparse arrays. This talk reviews some of Lisa’s contributions to adaptive beamforming and highlights how some of her contributions inspired research in the UMass Dartmouth Signal Processing Group. [Work supported by ONR Code 321US.]

Signal Processing Algorithm and System Realization of Broadband Parametric Array Sub-Bottom Profiler

Parametric acoustic array can generate a low frequency narrow sound beam with sufficient penetrating ability with a small size transducer. Besides, the generated beam has almost no side lobe, and it is easy to form a wide band signal at low frequencies, thus effectively improving the signal processing gain. Therefore, broadband parametric array shows advantages in shallow water sub-bottom profiling. In this paper, the signal processing algorithm of transmitting end and receiving end of parametric array sonar was studied, and a parametric array sub-bottom profiler was designed and realized for experimental verification. Based on Berktay’s far-field solution, the performance of different modulation methods was analyzed and compared, and suppressed carrier double-sideband modulation was chosen as the optimal modulation method. Furthermore, corresponding echo signal processing algorithm was designed to improve the detection resolution. According to lake trials of the broadband parametric array sub-bottom profile measurement system as designed, a clear sub-bottom profile was obtained, which verified the system’s effectiveness.

Direction of Arrival Estimation of Acoustic Sources with Unmanned Underwater Vehicle Swarm via Matrix Completion

With the rapid development of vibration and noise reduction technologies, underwater target detection is facing great challenges. Particularly, the task of high-resolution direction of arrival (DOA) estimation with sonar array is becoming more and more tough. In recent years, unmanned underwater vehicles (UUVs) have been developed considerably, with the improvements of target localization performance in terms of adaptability, detection range, operation efficiency, and anti-interference ability. Nevertheless, in general, the size of UUV is small such that current passive sonar systems usually have relatively limited localization accuracy, detection distance, and environmental robustness in complex ocean noise. This motivates us to present a new approach to construct a large-aperture virtual array with multiple small-aperture arrays of unmanned underwater vehicle swarm (UUVS) which consists of multiple UUVs in this paper. However, for the UUVS array, the received data could suffer from unobserved and corrupted samples. This makes it challenging to analyze and process large-aperture array data. Towards this end, the matrix completion technique is employed to recover the unobserved and corrupted data for virtual array construction based on the low rank property of array data matrix. The recovered matrix is then exploited for underwater target bearing estimation using the traditional DOA estimation approach. Numerical results verify that the proposed method is capable of detecting underwater targets with high precision and resolution.

Sparse Bayesian learning-based spatial spectrum estimation for mobile sonar platforms during turning

Mobile sonar platforms usually experience turning in the course of travel. During turning, due to the change of heading, the steering vector of the sonar array is not constant, which will cause the widening of the spatial-spectral peaks or even fail to estimate target bearings. To deal with this problem, this paper first establishes a multi-snapshot fusion equation for observed data from different heading angles. Then, a sparse Bayesian learning-based method is utilized to solve the fusion equation and provides the estimate of the spatial spectrum. The simulation results exhibit that the proposed method can resolve the port and starboard ambiguity problem and provide high estimation accuracy with enough heading change information. The sea trial results validate its feasibility and stability for estimating far-field target bearings in practical applications.

The Effects of Vertical Correlation Characteristics on Vertical Array Gain Performance in Deep Water

Spatial correlation characteristics of sound field have significant impact on array gain. Compared with the single-sensor receiving scenario, the vertical array with multiple receivers can remarkably enhance signal-to-noise ratio and detection ability. For the large-scale vertical array deployed in the incomplete deep-water sound channel, different bathymetry conditions or source-receiver ranges lead to different vertical correlation patterns, thus leading to the variation of array gain. Based on the deep-water experimental data conducted in the South China Sea, the effects of the vertical correlation on array gain performance are studied in both the flat and the uneven bottom environments. In the flat bottom environment, three representative ranges in the direct arrival zone and first shadow zone are chosen, respectively, to analyze how vertical correlation characteristics would affect the array gain performance. In the uneven bottom environment, attention is confined to those ranges that endure significant transmission loss differences when compared with the flat bottom environment. In summary, when the correlation coefficients grow smaller, it is observed that the array gains of different beamformers decrease, along with the energy drifting away from the first few eigenvalues and dispersing on many other eigenvalue elements. At the same time, the gap between the array gain results of eigenvalue beamforming and optimal quadratic beamforming would grow wider and the effective rank of signal covariance matrix would sprawl as well. The study is of great significance to analyze and enhance vertical sonar array detection performance at different ranges under various environmental conditions in deep water.

Fault Detection and Prognostic Health monitoring of Towed array sonars

Sonars are used to detect underwater targets and are especially important in maintaining naval superiority. Towed array sonars can operate at very low frequencies thus giving larger ranges and can be deployed to any desired depth of operation. Towed array sonars offer long range surveillance capability and is the sensor of choice for sustained surveillance operations. Reliable operation and maintenance of towed array sonars need effective methods of health monitoring and reliability prediction. For any prognostic health monitoring to be done we need to identify certain parameters which can be observed and will give system health status in the present condition. This paper proposes some metrics which are easily measurable in-situ and which offer insights into the health of the sonar system. These metrics give direct measureable impact for each of the failure modes and offer insights into the current health of an operational towed array sonar. Simulation results are shown to demonstrate the effectiveness of the proposed metrics and detailed trial data results from different towed array trials are analysed to validate them in operational scenarios.

Are universal beamformers passive cognitive sonar systems?

Cognitive sonars dynamically tune system parameters to improve performance in pursuit of specific goals. Most research on cognitive sonar focuses on active sonar, varying the transmitted waveform and ping rate. Although passive sonar also faces the challenges of pursuing goals in varying and unknown environments, little research has been done exploring cognitive approaches to passive beamforming for sonar arrays. Universal beamformers provide one implementation of a cognitive approach to passive sonar. Practical adaptive beamformers generally regularize the sample covariance matrix before estimating the array weights. Universal beamformers blend the array weights across a family of beamformers competing on different choices for the regularization parameters. The blend of array weights is performance driven based, including the largest portion from the beamformers best suited for the current environment. Universal are “doubly adaptive” in that each competing beamformer is adapting its array weight vector in response to the data observed at the sensor array, the universal algorithm is then meta-adapting the blend of these array weight vectors used to process the data based on the performance of each competing beamformer. We will present examples of beamformers, which are universal over dominant subspace dimension and beam pattern notch width for moving interferers. [Work supported by ONR 321US.]

Laboratory and Field Experimental Study of Underwater Inflatable Co-prime Sonar Array (UICSA)

This paper discusses the design and initial testing of a novel hydrophone array system dubbed the Underwater Inflatable Co-prime Sonar Array (UICSA). The UICSA will be a crucial component of an underwater deployable sensing network that can be rapidly deployed using compact autonomous underwater vehicles (AUVs). The UICSA initially is packed in a compact container to fit the payload space of an AUV. After deployment, the UICSA expands to its predetermined full length to acquire sensing data for source localization. More specifically, the mechanical compression of the UICSA is achieved through a non-rigid array support structure, which consists of flexible inflatable segments between adjoining hydrophones that are folded in order to package the UICSA for deployment. The system exploits compression in hydrophone layouts by utilizing a sparse array configuration, namely the co-prime array since it requires fewer hydrophones than a uniform linear array of the same length to estimate a given number of sources. With two-way compression, the storage, handling, and transportation of the compactly designed UICSA is convenient, particularly for the AUVs with limited payload space. The deployment concept and process are discussed, as well as the various UICSA designs of different support structures are described. A comparison of the various mechanical designs is presented and a novel hybrid-based expansion prototype is documented in detail. Laboratory study results of the UICSA prototype are presented that include water-swollen material tests in a pressurized environment and water tank validation of the inflation process. The UICSA prototype also has been deployed in the Harbor Branch channel to validate the performance, the related field test details and source localization results.

Cancellation of Towing Ship Interference in Passive SONAR in a Shallow Ocean Environment

Towed array sonars are preferred for detecting stealthy underwater targets that emit faint acoustic signals in the ocean, especially in shallow waters. However, the towing ship being near to the array behaves as a loud target, introducing additional interfering signals to the array, severely affecting the detection and classification of potential targets. Canceling this underlying interference signal is a challenging task and is investigated in this paper for a shallow ocean operational scenario where the problem is more critical due to the multipath phenomenon. A method exploiting the eigenvector analysis of spatio-temporal covariance matrix based on space time adaptive processing is proposed for suppressing tow ship interference and thus improving target detection. The developed algorithm learns the interference patterns in the presence of target signals to mitigate the interference across azimuth and to remove the spectral leakage of own-ship. The algorithm is statistically analyzed through a set of relevant metrics and is tested on simulated data that are equivalent to the data received by a towed linear array of acoustic sensors in a shallow ocean. The results indicate a reduction of 20-25dB in the tow ship interference power while the detection of long-range low SNR targets remain largely unaffected with minimal power-loss. In addition, it is demonstrated that the spectral leakage of tow ship, on multiple beams across the azimuth, due to multipath, is also alleviated leading to superior classification capabilities. The robustness of the proposed algorithm is validated by the open ocean experiment in the coastal shallow region of the Arabian Sea at Off-Kochi area of India, which produced results in close agreement with the simulations. A comparison of the simulation and experimental results with the existing PCI and ECA methods is also carried out, suggesting the proposed method is quite effective in suppressing the tow ship interference and is immensely beneficial for the detection and classification of long-range targets.

RIEMANNIAN MATCHED FIELD PROCESSING

As far as underwater source localization is concerned the Matched Field Processing (MFP) is an effective method. Floating ship localization, submarine localization in military section and fish finding in civilization are considered as the main application of MFP. Besides, determining environmental parameters such as sound speed profile, bottom topography and array tilt are also developed. Some methods such as empirical mode decomposition, adaptive MFP, compressive MFP and especially stochastic MFP using Riemannian geometry (RMFP) have been introduced recently in order to increase MFP’s reliability and resolution. It seems that the RMFP is the strongest candidate for the future development of MFP since it is inherited the strong foundation of both MFP and Riemannian Geometry. Surprisingly, not only the nature of curvature of sound wave but also the nature of MFP are exploited in RMFP. The aim of this monograph is introduce RMFP by considering the Riemannian distance instead of Euclidean distance. Two approaches of RMFP construction, i.e., isometric mappings and direct Riemannian distance calculation are introduced. The organization of this monograph is as follows. Two first chapters of this monograph revised the reader about the essential meaning of Gauss Curvature, Geodesic equation, iso-metric mapping in Riemannian Geometry and the state of the art of MFP. Chapter 3 presents Riemannian MFP. Chapter 4 concludes the monograph with discussions about the performance of MFP. This monograph is designed for graduated students, scientists and senior engineers who working in the field of underwater acoustic engineering. We would like to thanks SACLANTC for providing access of SONAR array data. We also express our gratitude to University of Engineering and Technology (VNUH) for partial financial support this monograph. Finally, I deeply express my appreciate to my family, especially my father for their patient and love to me.

Variation of signal-to-noise ratio of vertical array with sound source depth under wind-generated noise background

Wind-generated noise is ubiquitous in ocean environments and highly influences the passive sonar performance. Since it originates from sources near the ocean surface, one of its physical features is that it largely represents only the intermediate- and high-order modes. The array-level signal-to-noise ratio (SNR), which includes the array-sampled sound intensity, background noise power, and array gain, is an essential quantity determining the sonar array performance. What is investigated in this work is how the array-level SNR of the vertical line array (VLA) varies with the source depth in downward-refracting shallow water, contributed by the modal structure of the surface noise. On the assumption that the modes are well sampled, it is theoretically demonstrated that the SNR varying with the source depth can be approximated as a linear combination of the lower-order mode-amplitude intensities varying with the water depth. Particularly, when the surface noise especially dominates and the water channel is highly downward refractive, this variation can be represented nearly only by the 1<sup>st</sup>-order mode-amplitude intensity varying with depth. The structure is meaningful in practice. It suggests the SNR will be inherently larger when the source is submerged than it is near the ocean surface, and will be maximized at a source depth slightly below the 1<sup>st</sup>-order mode peak across different source ranges. The above assertions are demonstrated in a typical downward-refracting shallow-water channel; the effects from the dominant degree of the surface noise, sound speed gradient in water column, and array aperture are investigated numerically. The obtained results are shown below. 1) Under certain circumstances, the variation of SNR with source depth is nearly irrelevant to the source range. 2) When the surface noise is more significant, the largest SNR in a certain source range will be more significantly larger than the SNR for the source near the surface, the corresponding source depth will be closer to that presenting the 1<sup>st</sup>-order mode’s peak, and the variation of SNR with source depth is increasingly irrelevant to the source range. 3) A stronger downward-refracting sound speed also enhances this SNR superiority and irrelevance to the source range, but causes the source depth presenting the largest SNR to be more deviated from the 1<sup>st</sup>-order mode’s peak. 4) Although the structure is unraveled on the assumption that the VLA spans the full water column, it can be seen when the VLA does not but covers the low-order modes' main part; when the array aperture is insufficiently large it will become approximately periodic in the source range, with the source depth presenting the largest SNR fluctuating lightly and nearly periodically around the 1<sup>st</sup>-order mode peak.