Abstract The validation of seafloor scattering models used for seabed characterisation requires quantifying the contributions from the sediment interface and volume to the total acoustic returns. At ...low‐frequencies, direct measurements of sediment volume scattering have rarely been made, due to the bias in interface roughness scattering caused by large beamwidths of low‐frequency sonars. Endfire Synthetic Aperture Sonar (EF‐SAS) can achieve narrower beamwidths by forming a vertically oriented synthetic array as a transmitter and/or receiver and moving it through the water column. The narrower beamwidths achieved by EF‐SAS allow for more accurate measurements of volume scattering by reducing interface scattering bias in acoustic returns. The application of EF‐SAS for sediment characterisation is explored for the first time. The authors demonstrate that EF‐SAS can be used to construct the angular response curve for both interface and volume scattering as well as to estimate the attenuation and reflection coefficients, which can be inverted for grain size.
Seafloor sediment acoustic returns are comprised of scattering from both the sediment-water interface and the sediment volume. For low frequencies, volume scattering is often the dominant mechanism; ...however, direct measurements of this component have rarely been made, owing to the large beamwidths typically associated with low-frequency echosounder systems. When beamwidths are large, the sediment interface and volume returns arrive at the same time, causing estimates of volume scattering to become biased by the interface scattering. Endfire synthetic aperture sonar (EF-SAS) can achieve narrower beamwidths by coherently combining multiple acoustic returns as a vertically oriented transmitter and/or receiver is moved toward the seafloor. This article will present a proof of concept for EF-SAS, which will include an analysis of EF-SAS processing methods and an experimental verification of EF-SAS beamwidth improvements. Results from a motion-controlled EF-SAS experiment indicate that EF-SAS gains can be accurately predicted and that these gains are significant enough to remove the interface scattering bias.
Seabed image segmentation is an important product for a variety of fields, including, habitat mapping, geological surveys, mine counter measures, and naval route planning. Developing a clustering ...algorithm that can both accurately segment high resolution imagery and generalize well over large areas is challenging. In this paper we will evaluate the performance of a new unsupervised image segmentation algorithm. The method utilizes imagery derived features (intensity and texture) to identify clusters (different seabed types) in feature space while also encouraging local homogeneity. In this paper we will demonstrate how our spatially coherent k-means clustering algorithm can efficiently and accurately segment Synthetic Aperture Sonar (SAS) images. Our experiments show that our spatially coherent clustering algorithm can increase segmentation accuracy up to 15 and 20 percent relative to OpenCV k-means and ArcGIS Pro ISO clustering, respectively.
The focusing of a synthetic aperture requires precise phase alignment of signals that are combined coherently to form an image. Synthetic aperture sonars are typically operated using multi-channel ...receiver arrays to maximize the achievable area coverage rate, which places strict requirements on calibration accuracy of the acoustic elements. Conventional sonar calibration is a time-consuming process requiring specialized facilities, and array calibration may change due to environmental conditions such as temperature and operating depth. In this paper, we describe an in situ calibration technique for an array installed in an autonomous underwater vehicle using backscatter from diffuse targets on the seabed as the calibration source. The technique includes compensation for vehicle motion and and range-variant corrections for bistatic geometry and phase curvature of the received wavefield. Results are presented for a modular receiver array with an installation-induced phase error that caused image blurring and grating lobes. The calibration technique quantified the installation offset and restored the imagery to its properly focused state. A simulation of the sonar impulse response is also presented to confirm that the measured phase error was the root cause of the observed image artifacts.
Synthetic aperture sonar (SAS) is an acoustic imaging technique capable of providing constant resolution over a wide swath. Similar to sidescan sonar, SAS has a coverage gap directly below the system ...known as the nadir gap. Developing nadir gap coverage technologies for SAS is challenging because it is difficult to match its altitude and resolution capabilities. In this paper, we introduce a unique gap reduction technique that combines a short-range SAS designed to reduce the nadir gap, with an auxiliary sensor to provide complete nadir gap coverage. Two different auxiliary sensors are investigated: a 3D laser profiler and a multibeam echosounder. Results indicate that all three gap fill solutions are capable of providing high (centimetric) resolution for a high speed towed SAS system operating at 10 m altitude. When combined, the sensors can provide full swath coverage that results in a 33% increase in area coverage rate.
Manually identifying objects in synthetic aperture sonar (SAS) imagery is costly and time consuming, making identification through computer vision and deep learning techniques an appealing ...alternative. Depending on the application, a generalized map (semantic segmentation) and/or a characterization of each individual object (object detection) may be desired. Here, we demonstrate a framework that allows us to simultaneously generate both semantic segmentation maps and object detections with a single deep learning model by chaining together a U-Net model with k-means clustering and connected components. This framework streamlines the model training phase by allowing us to utilize a set of semantically segmented training data to yield both semantic segmentation and bounding box predictions. We demonstrate that the deep learning model can achieve accurate predictions with a small training set through transfer learning from a convolutional neural network pretrained on optical imagery. Results from this unified framework will be presented on images of boulders collected during various surveys using a Kraken Robotics miniature SAS (MINSAS).
Interferometric Synthetic Aperture Sonar (InSAS) measurements are noisy and typically require multiple estimates of the scene depth to constrain the vertical uncertainty (vertical resolution). Many ...InSAS systems sacrifice horizontal resolution for vertical resolution by utilizing spatial averaging. In previous work, we demonstrated a technique termed Seamless SAS that can form multiple observations of each image pixel, allowing us to substantially reduce the amount of spatial averaging required by supplementing it with ensembles. This paper is an extension of our previous work, focusing on furthering our theoretical understanding of the ensemble technique by analyzing the coherence between adjacent pings and synthetic aperture image strips collected by the Kraken Miniature Interferometric Synthetic Aperture Sonar (MINSAS).
Naval operations such as IPOE (Intelligence Preparation of Operational Environment) and explosive ordnance detection often requires high-resolution surveys in shallow water and surf zones. Surveying ...in such shallow water presents a variety of operational challenges. The two fundamental constraints to operating in shallow water are vehicle size and performance degradation related to environmental constraints. To effectively access shallow water, lightweight man-portable Autonomous Underwater Vehicles (AUVs) are often desired. The sonar system design and processing must also be equipped to handle the challenges of the very shallow water environment, which include sloping seabed surfaces, strong multi-path environments, and highly nonlinear vehicle motion, all of which can impair target detection and analysis. Synthetic Aperture Sonar (SAS) performance can be optimized using performance prediction models; however, currently available performance models do not sufficiently include the challenges of working in shallow water. In this paper we will develop a shallow water SAS performance model and validate the results of SAS performance from two different shallow water surveys conducted using Kraken's Miniature Synthetic Aperture Sonar (MINSAS). The results show that the MINSAS is particularly adept in shallow water conditions where its narrow vertical beamwidths and coherent integration help reject multipath and enhance target detection.
Seafloor sediment acoustic returns are comprised of scattering from both the sediment-water interface and the sediment volume. At low-frequencies, volume scattering is often the dominant mechanism; ...however, direct measurements of this component have rarely been made, owing to the large beamwidths typically associated with low-frequencies. When beamwidths are large, the sediment interface and volume returns arrive at the same time, causing estimates of volume scattering to become biased by the interface scattering. End-Fire Synthetic Aperture Sonar (EF-SAS) can achieve narrower beamwidths by coherently combining multiple acoustic returns as a vertically oriented transmitter and/or receiver is moved towards the seafloor. A beampattern model developed for EF-SAS suggests EF-SAS processing can reduce the beamwidth of a sonar by a factor of five with an array length of 100 wavelengths. In this thesis, EF-SAS processing methods were developed and the resulting gains were experimentally verified through a motion-controlled EF-SAS field trial. Experimental tests indicate that EF-SAS gains can be accurately predicted using the EF-SAS beampattern model. Analysis of the data demonstrates the ability of EF-SAS to reduce interface scattering. EF-SAS can produce a variety of data products relevant to seafloor characterization, including: the angular response of interface and volume scattering, the sediment reflection coefficient, and the sediment attenuation coefficient. This thesis concludes with recommendations for the development of a specialized EF-SAS tool, and an analysis of its applications.
When hunting for stealthy naval mines in highly cluttered regions, a multi-aspect capability is advantageous because multiple "looks" improve the probabilities of detection and classification. For ...autonomous underwater vehicles (AUVs), full azimuth coverage is possible by flying circular or spiral trajectories while keeping the target within the sonar beam. Circular navigation of a towed sensor is more challenging due to the variable tow cable dynamics. Nevertheless, towed synthetic aperture sonar (SAS) systems are preferred for many applications because they achieve a very high area coverage rate with excellent endurance, rapid transit capabilities, and high bandwidth telemetry. In this paper, we describe and demonstrate an operational concept called "Square SAS" for towed sonar platforms. Square SAS consists of fusing multi-aspect imagery from piecewise linear survey lines having an azimuthal extent of at least ±90°. The concept can be extended to include an arbitrary number of piecewise linear survey lines such as hexagonal or octagonal configurations. Experimental results are presented from Kraken's SeaScout seabed mapping system equipped with a high speed towed SAS. The image fusion technique is generally applicable to any SAS platform, including AUVs, without any prior assumption for the target shape. However, the technique is ideal for towed systems where multiple linear passes are necessary to achieve a true multi-aspect imaging capability with a wide azimuth coverage for effective target classification.
