Synthetic Aperture Sonar Data Research Articles

Model selection techniques for seafloor scattering statistics in synthetic aperture sonar images of complex seafloors

AbstractIn quantitative analysis of seafloor scattering measurements, it is common to model the single‐point probability density function of the scattered intensity or amplitude. For more complex seafloors, the pixel amplitude distribution has previously been modelled with a mixture model consisting of two K distributions, but the environment may have more identifiable scattering mechanisms. Choosing the number of components of a mixture model is a decision that must be made, using a priori information, or using a data driven approach. Several common model selection techniques from the statistics literature are explored (the Akaike, Bayesian, deviance, and Watanabe‐Akaike information criteria) and compared to the authors' choice. Examples are given for synthetic aperture sonar data collected by an autonomous underwater vehicle in a rocky environment off the coast of Bergen, Norway, using the HISAS‐1032 synthetic aperture sonar system. The Bayesian information criterion aligned most closely with the interpretation of both the acoustic images and the plots of the probability of false alarm.

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 Statistical Evaluation of the Connection between Underwater Optical and Acoustic Images

The use of Synthetic Aperture Sonar (SAS) in autonomous underwater vehicle (AUV) surveys has found applications in archaeological searches, underwater mine detection and wildlife monitoring. However, the easy confusability of natural objects with the target object leads to high false positive rates. To improve detection, the combination of SAS and optical images has recently attracted attention. While SAS data provides a large-scale survey, optical information can help contextualize it. This combination creates the need to match multimodal, optical–acoustic image pairs. The two images are not aligned, and are taken from different angles of view and at different times. As a result, challenges such as the different resolution, scaling and posture of the two sensors need to be overcome. In this research, motivated by the information gain when using both modalities, we turn to statistical exploration for feature analysis to investigate the relationship between the two modalities. In particular, we propose an entropic method for recognizing matching multimodal images of the same object and investigate the probabilistic dependency between the images of the two modalities based on their conditional probabilities. The results on a real dataset of SAS and optical images of the same and different objects on the seafloor confirm our assumption that the conditional probability of SAS images is different from the marginal probability given an optical image, and show a favorable trade-off between detection and false alarm rate that is higher than current benchmarks. For reproducibility, we share our database.

Deep learning‐based time delay estimation for motion compensation in synthetic aperture sonars

AbstractAccurate and robust time delay estimation is crucial for synthetic aperture sonar (SAS) imaging. A two‐step time delay estimation method based on displaced phase centre antenna (DPCA) micronavigation has been widely applied in motion estimation and compensation of SASs. However, the existing methods for time delay estimation are not sufficiently robust, which reduces the performance of SAS motion estimation. Deep learning is currently one of the cutting‐edge techniques and has brought about a remarkable progress in the field of underwater acoustic signal processing. In this study, a deep learning‐based time delay estimation method is introduced to SAS motion estimation and compensation. The subband processing is first applied to obtain ambiguous time delays between adjacent pings from phases of SAS echoes. Then, a lightweight neural network is utilised to construct phase unwrapping. The model of the employed neural network is trained with simulation data and applied to real SAS data. The results of time delay estimation and motion compensation demonstrate that the proposed neural network‐based method has much better performance than the two‐step and joint‐subband methods.

Aural based scene understanding for sonar applications

Acoustic returns scattered from either surfaces or objects are typically interpreted using image processing techniques such as beamforming and computer vision models such as convolutional neural networks. However, image representations can be burdensome to generate, creating delayed responses in real-time scenarios, and difficult to parse from the scene-understanding perspective. This work investigates direct exploitation of the information content from streaming, near-raw sonar time series data for the purpose of scene characterization. Summary statistics drawn from the sonar performance estimation community and communities that study aural-based perception are used to identify scene characteristics such as flat sand, ripples, gravel beds, or sea grass, in side-looking synthetic aperture sonar (SAS) data. An empirical multi-class classification study is presented that explores the performance of seafloor characterization methods on time series versus imagery.

Back Projection Algorithm for Multi-Receiver Synthetic Aperture Sonar Based on Two Interpolators

The back projection (BP) algorithm is characterized by its high performance for multi-receiver synthetic aperture sonar (SAS). For this reason, it is usually used to evaluate the imaging performance of Fourier-domain methods. However, this algorithm suffers from a large computation load, and the imaging efficiency is seriously lowered. In order to improve the imaging performance, this paper proposes focusing the multi-receiver SAS data using the BP algorithm based on two interpolators, including the linear interpolation and nearest-neighbor interpolation. The former interpolation is used to decrease the interpolation error based on adjacent sampled data; the latter estimates the data at the desired moment by assigning the data value of the nearest sample as estimated data. Then, the imaging performance of the presented method is discussed in detail based on simulations and real-data processing. With the presented method, the imaging performance can be improved without a loss of efficiency compared to nearest-neighbor interpolation without an upsampling operation. In comparison with the traditional BP algorithm, the presented method can be used to improve the imaging efficiency without any loss of performance.

Resolution dependence of acoustic scattering statistics for complex seafloors

Underwater Unmanned Vehicles (UUVs) utilize sonar perception to conduct sea floor mapping and target detection operations. However, cooperative surveys with multiple types of UUVs are difficult because platforms with different resolutions may generate different probability density functions (PDFs) of the magnitude of the complex pressure. An area of research that has not been adequately studied is the effects of resolution manipulation during the post-processing of high-resolution data from complex seafloor environments. This work analyzes synthetic aperture sonar (SAS) data collected from multiple seafloor geomorphologies surrounding Bergen, Norway to study the resolution dependence of scattering statistics for complex seafloors. Multi-look methods will be applied to reduce the resolution. The original data and reduced resolution data will be compared in terms of PDF amplitude and evaluated by standard goodness of fit tests with heavily tailed statistical models that are commonly used in the radar and sonar community, including mixture models. The goal of the paper is to provide a bridge to combining high-resolution and low-resolution sonar data together to enhance sonar perception. Data provided by the Norwegian Defense Research Establishment. [Funding provided by the Office of Naval Research.]

Modeling the effects of internal waves on synthetic aperture sonar resolution

Synthetic aperture sonar (SAS) beamforming is performed under the assumption of a constant sound speed within the water column or by utilizing a measured sound speed profile. Water column features, such as internal waves or internal wave boluses that cause a sound speed structure different than that assumed, can break the constant sound speed assumption resulting in image defocusing and loss of resolution. The resultant loss of resolution can be quantified using the point spread function (PSF). An ellipse fitting technique was applied to SAS images collected by the Norwegian Defence Research Establishment (FFI) using the HISAS system. In conjunction with the analysis conducted on real data, a ray tracing method was applied to model the broadening effects on the PSF for sound speed differences caused by internal wave boluses. A wavenumber beamforming algorithm was also applied to artificial echo data, in which sound speed errors were introduced. It is hoped that the two models can be used in conjunction with SAS data to determine the sound speed within an internal wave feature from the observed loss in resolution.

Model validation for simulated synthetic aperture sonar time series data

Synthetic aperture sonar (SAS) systems have grown in popularity due to their ability to create high-resolution sonar imagery with a high area coverage rate. Researchers continue to find new uses for SAS data products. To support the development of new uses and algorithms, it is important to be able to accurately simulate SAS systems with synthetic time series data. This simulated data can be used for many purposes, including estimating system signal-to-noise ratio and performance in a variety of environments, testing signal processing algorithms, and training algorithms to recognize objects in SAS imagery. For these processing tasks to be successful, the simulator must accurately represent physical phenomena in the oceanic underwater environment, which can be extremely complex. This presentation will outline a process for validation and verification of a high-frequency SAS time series simulator, and discuss several useful tests for validating model fidelity. Two fundamental concepts will be explored in depth: the sonar equation and spatial coherence theory. The sonar equation is important because it accounts for correct implementation of multiple lower level models such as geometry, beam patterns, propagation, and seafloor scattering strength. Spatial coherence is important because it is relied on to form coherent imagery.

An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal

When the multi-receiver synthetic aperture sonar (SAS) works with a wide-bandwidth signal, the performance of the range-Doppler (R-D) algorithm is seriously affected by two approximation errors, i.e., point target reference spectrum (PTRS) error and residual quadratic coupling error. The former is generated by approximating the PTRS with the second-order term in terms of the instantaneous frequency. The latter is caused by neglecting the cross-track variance of secondary range compression (SRC). In order to improve the imaging performance in the case of wide-bandwidth signals, an improved R-D algorithm is proposed in this paper. With our method, the multi-receiver SAS data is first preprocessed based on the phase center approximation (PCA) method, and the monostatic equivalent data are obtained. Then several sub-blocks are generated in the cross-track dimension. Within each sub-block, the PTRS error and residual quadratic coupling error based on the center range of each sub-block are compensated. After this operation, all sub-blocks are coerced into a new signal, which is free of both approximation errors. Consequently, this new data is used as the input of the traditional R-D algorithm. The processing results of simulated data and real data show that the traditional R-D algorithm is just suitable for an SAS system with a narrow-bandwidth signal. The imaging performance would be seriously distorted when it is applied to an SAS system with a wide-bandwidth signal. Based on the presented method, the SAS data in both cases can be well processed. The imaging performance of the presented method is nearly identical to that of the back-projection (BP) algorithm.

Late return focusing algorithm for circular synthetic aperture sonar data.

Synthetic aperture sonar processing is used to generate imagery for remote sensing applications such as environmental characterization and object detection. Images primarily represent the initial geometric response of acoustic scattering, but there are additional information embedded in the raw data that are not well-represented in images. For example, responses such as internal multiple scattering or elastic scattering are delayed in time, and they appear defocused in imagery. A complementary processing algorithm is presented that improves the focus of late acoustic scattering responses, which can potentially provide additional information about the object and aid data interpretability.

New insights into the formation of submarine glacial landforms from high-resolution Autonomous Underwater Vehicle data

Autonomous Underwater Vehicles (AUVs) deployed close to the seafloor can acquire high-resolution geophysical data about the topography and shallow stratigraphy of the seabed, yet have had limited application within the fields of glacial geomorphology and ice sheet reconstruction. Here, we present multibeam echo-sounding, side-scan sonar, sub-bottom profiler and High-Resolution Synthetic Aperture Sonar (HISAS) data acquired during three AUV dives on the northeast Antarctic Peninsula continental shelf. These data enable glacial landforms, including mega-scale glacial lineations (MSGLs), grounding-zone wedges (GZWs) and iceberg ploughmarks, to be imaged at a horizontal resolution of a few tens of centimetres, allowing for the identification of subtle morphological features. We map tidal ridges that are interpreted as having been formed 1) along the ice-sheet grounding line by the squeezing up of soft seafloor sediments by vertical motion of the grounding line during tidal cycles, and 2) by the tidally driven motion of grounded or near-grounded icebergs. These data also enable the mapping of small GZWs that show the location of short-term still-stands or re-advances of the ice-sheet grounding zone. No meltwater channels are identified from our data, suggesting that free-flowing meltwater may not be essential for the formation of GZWs or MSGLs. The examples presented here show how high-resolution AUV-derived geophysical data provide a step-change in our ability to image seafloor glacial landforms, enabling new interpretations about past ice dynamics and glacial sedimentation at fine temporal and spatial scales.

A Novel Framework for Evaluating Performance-Estimation Models

A general framework for quantifying the worth of a performance-estimation model is proposed. The purpose of the model is to predict the performance of an automatic target recognition algorithm on a given set of test data, while the purpose of the framework is to quantify how well the model fulfills its task. To this end, a quantity referred to as the utility, which is based on the Kullback–Leibler divergence, is introduced. A key aspect of the framework is the inclusion of a significance function that specifies the relative importance of each point in the performance space, here assumed to be defined in terms of false alarm rate and probability of detection. Example significance functions are suggested and discussed. The functionality of the proposed framework is demonstrated on an underwater target detection application involving measured synthetic aperture sonar data. In this context, an image complexity metric is exploited to enable the development of models corresponding to different seafloor conditions and mine-hunting difficulty. The appeal of the framework is its ability to quantitatively assess the utility of competing performance-estimation models and to fairly compare the utility of a model on different test data sets.

Imaging Algorithm for Multireceiver Synthetic Aperture Sonar

This paper presents a range migration algorithm for the multireceiver synthetic aperture sonar (SAS) based on Loffeld’s bistatic formula, which consists of the quasi monostatic term and the bistatic deformation (BD) term. It is expected that the multireceiver data can be easily focused by the monostatic SAS imaging algorithm after compensating the BD term. In this paper, the BD term is first decomposed into the range variant phase and the range invariant phase. At this point, the BD term is compensated via two steps. One step is the compensation of the range variant phase through the range-dependent sub-block processing in the 2-D frequency domain. The other one is the arrangement of the multireceiver data in a sequential order in the 2-D time domain. After these operations, we obtain the monostatic SAS equivalent data, which is viewed as the input of the traditional range migration algorithm. The processing results of the simulated data and the real data show that the proposed method can work well for the focusing of the multireceiver SAS data. More importantly, the presented method dramatically improves the imaging efficiency without loss of performance since the complex multiplication and the data arrangement are only exploited by the presented method.

Wavenumber domain acceleration for stripmap fast factorized backprojection beamforming

Synthetic aperture sonar (SAS) imaging is frequently used in conjunction with autonomous underwater vehicles (AUV's) to perform high resolution wide-area surveys. Wave action and currents perturb the trajectories of AUV's performing these surveys and these perturbations have important ramifications for the SAS beamforming process. Generalized beamforming algorithms operating in the time domain are very flexible with regard to trajectory non-linearities, but they also tend to have a higher computational cost than alternative frequency domain algorithms. Of the generalized beamformers one of the more efficient is fast factorized backprojection (FFBP), which leverages sub-aperture processing and a series of coordinate transformations to improve computational efficiency. FFBP can be used to beamform SAS data, however many SAS systems make use of a real aperture to boost range potential. The existence of a real aperture implies that a frequency domain beamformer could be used to bypass the first few stages of FFBP without loss of generality; however, standard frequency domain SAS beamforming algorithms are not compatible with the FFBP framework. A wavenumber domain beamformer compatible with the early-stage FFBP framework is derived and beamforming results for the modified FFBP algorithm are compared with the standard approach that operates entirely in the time domain.

A Squint Multi-aperture Chirp Scaling Algorithm for the Multi-aperture SAS with Small Squint Angle

Squint multi-aperture synthetic aperture sonar (SAS) data is more challenging to process than data about its common side-looking counterpart because the geometry is complicated. While traditional multi-aperture algorithms mainly handle general side-looking cases, they do not work well for squint cases. In this paper, a squint multi-aperture chirp scaling algorithm (SMACSA) is proposed that is suited to process multi-aperture SAS data with small squint angle. In the derivation of the imaging algorithm, the squint multi-aperture signal is converted into the squint monostatic signal by the phase compensation factor of range dependent and the delay compensation factor on reference range, then the multi-aperture chirp scaling algorithm (SMACSA) is first proposed by drawing on the classic chirp scaling algorithm (CSA). The greatest improvement between SMACSA and CSA is the azimuth walk introduced by non-stop-hop-stop mode. Finally, the validity of SMACSA is proved by computer simulation.

Simulating an object response with synthetic aperture sonar inverse imaging

Synthetic aperture sonar (SAS) algorithms were developed in order to produce high resolution imagery from wide-beam wideband data. The high data rates associated with SAS data have fostered the development and implementation of efficient beamforming algorithms, many of which were originally developed for synthetic aperture radar. Some of these beamforming algorithms are invertible, and inverse beamforming techniques have been developed for SAS data analysis, where imagery is inverted in order to produce aspect-dependent data. Since imagery is invertible, this process can also be used as a simulation to produce stave-level data from a simulated image. This paper describes a modeling approach where inverse imaging techniques are applied in order to generate simulated stave-level sonar data. Using this approach, an image is generated from assumed object geometry and reflectance and inverted in order to generate stave-level data. The stave level data is then beamformed specifically to produce frequency vs. aspect data products. Since the inverse imaging process assumes specular reflection, features present in the frequency vs. aspect data are solely due to the surface reflectance and object shape. Comparison of inverse image simulated data with physics based simulations or field-collected data are shown and the limitations of the comparison are discussed.

A Nonlinear Chirp Scaling Algorithm for Processing Multi-Receiver SAS Data with Small Squint Angle

Squint multi-receiver synthetic aperture sonar (SAS) data is more challenging to process than data about its common side-looking counterpart because the geometry is complicated. While traditional multi-receiver algorithms mainly handle general side-looking cases, they do not work well for squint cases. Up to now, no multi-receiver algorithm for handling squint multi- receiver SAS data has been reported. This paper focuses on an imaging algorithm for the squint multi-receiver SAS. Firstly, the “non-stop-go-stop” signal model is established for squint multi- receiver SAS. Then a squint multi-receiver nonlinear chirp scaling algorithm (SMANLCSA) is proposed that is suited to process multi-receiver SAS data with small squint angle and SMANLCSA involves three key steps. The first step is to turn squint multi-receiver data into squint single-receiver data. The second step is a Doppler centroid frequency shift. The third step is range cell migration correction using nonlinear chirp scaling. Simulations have shown that SMANLCSA can handle squint multi-receiver data more accurately and effectively than the previous algorithm..

Impact of temporal Doppler on synthetic aperture sonar imagery.

Synthetic Aperture Sonar (SAS) coherently processes the acoustic data acquired along a linear trajectory. The imaging process is in essence an inverse problem where the reflectivity of the seafloor has to be estimated. Several imaging algorithms have been proposed over the years including back-projection algorithms. One commonly assumed hypothesis, however, is that the antenna is motionless during transmission and reception. This hypothesis is known as the start-stop assumption. This paper questions the validity of this assumption, and proposes a full derivation of the SAS processing taking into account the vehicle motion by using the Lorentz transformation. The cell migration for the SAS system is computed and the validity limit of the stop-start assumption depending on the SAS system parameters is derived. Numerical examples of start-stop assumption violations are given and the Doppler cell migration correction on real SAS data are presented and discussed.

Archaeological use of Synthetic Aperture Sonar on deepwater wreck sites in Skagerrak

Marine archaeological surveying in deep waters has so far been challenging, mainly due to operational and technological constraints. The standard tool has been Side Scan Sonar (SSS) towed behind a surface vessel. Synthetic Aperture Sonar (SAS) technology is not subject to the traditional range/resolution trade-off, and produces results of considerably higher quality than traditional SSS. In 2015 and 2016 a comprehensive mapping of wrecks in Skagerrak, a large deepwater area off the south coast of Norway was undertaken, using an interferometric SAS system deployed on an autonomous underwater vehicle. By examining data from two passes of one of the many historical wrecks that were detected in the survey area, we demonstrate how SAS can be used to produce very high resolution imagery and bathymetry of wreck sites. Furthermore, post processing techniques are applied to exploit the high information content inherent in SAS data, enhancing aspects of the data for relevant archaeological analysis and interpretation. We show in this paper how SAS technology represents significant improvements in our abilities to conduct high quality and high resolution seabed mapping. The adoption of this technology will both benefit archaeological research and provide knowledge for better decision making in underwater cultural heritage management.