Low probability of detection (LPD) is an extremely important characteristic of an underwater acoustic communication (UWAC) system when used for military-related applications, since the detection of a communication signal in the channel may reveal the presence of the transmitter or receiver. Furthermore, the recent advances in the understanding of the environmental effects of sound transmission in the ocean have led to a growing interest in LPD for UWAC also for civilian use. This is because systems that are designed for reliable communication at low signal power have a reduced environmental impact. In this paper, we identify the main challenges for the design of UWAC LPD systems. We describe and classify common approaches for transmission, reception, and interception of LPD signals, and we discuss their advantages and weaknesses. We also present several methods to determine the LPD capability of a system and suggest to adopt the range ratio test as a performance measure that captures the effects of signal propagation through the UWAC channel and the capabilities of the communication receiver and a signal interceptor. In light of the environmental benefits of LPD transmission and ongoing discussions about limiting the power spectral density of UWAC signals through regulations, we believe that LPD transmission is an area of growing importance for UWAC research and development. We hope that this paper serves as a motivation and a starting point for further research in this field.

Long-range underwater acoustic communication (LR-UWAC) refers to the peer-to-peer transmission of messages for distances of tens to hundreds of km. It is a key enabling technique for applications such as control over unmanned underwater vehicles for long-term surveying. While underwater acoustic communication over shorter ranges is an established technique, this is not the case for LR-UWAC. This gap is mostly due to channel uncertainties: in the absence of feedback from the receiver and due to the long transmission range, channel state information (CSI) at the transmitter may not reflect the actual channel. In this paper, we propose an adaptive approach to pre-set the modulation scheme for LR-UWAC. This is a channel classification approach which, based on environmental information and on prior training on various channel types, predicts the best modulation scheme for the expected channel. Our classification procedure is trained to identify the channel’s important features. Thus, compared to a direct decision approach, it becomes less sensitive to possible mismatches of environmental information. Our numerical simulation and sea experiment show that our approach successfully identifies the best modulation scheme based on the environmental information - even when the information is biased or only partially available.

With the growing use of underwater acoustic communication (UWAC) for both industrial and military operations, there is a need to ensure communication security. A particular challenge is represented by underwater acoustic networks (UWANs), which are often left unattended over long periods of time. Currently, due to physical and performance limitations, UWAC packets rarely include encryption, leaving the UWAN exposed to external attacks faking legitimate messages. In this paper, we propose a new algorithm for message authentication in a UWAN setting. We begin by observing that, due to the strong spatial dependency of the underwater acoustic channel, an attacker can attempt to mimic the channel associated with the legitimate transmitter only for a small set of receivers, typically just for a single one. Taking this into account, our scheme relies on trusted nodes that independently help a sink node in the authentication process. For each incoming packet, the sink fuses beliefs evaluated by the trusted nodes to reach an authentication decision. These beliefs are based on estimated statistical channel parameters, chosen to be the most sensitive to the transmitterreceiver displacement. Our simulation results show accurate identification of an attacker’s packet. We also report results from a sea experiment demonstrating the effectiveness of our approach.

We offer a new unsupervised statistically-based algorithm for the detection of underwater objects in synthetic aperture sonar (SAS) imagery due to its high-resolution imagery and because its resolution is independent of the range. In contrast to other methods that do not utilize the statistical model of the shadow region, our algorithm combines highlight detection and shadow detection using a weighted likelihood ratio test, while exploiting the expected spatial distribution of potential objects. We detect highlights by a higher-order-statistics representation of the image, followed by a segmentation process to form a region-of-interest (ROI). Then, while taking into account the sonar elevation and scan angle, for each ROI, we use a support vector machine (SVM) over the statistical features of the pixels within the ROI to detect shadow-related pixels and background pixels. Our algorithm has the benefit of being robust as a result of setting its main parameters in situ. Moreover, we do not require knowledge about the target's shape or size, thereby making our algorithm suitable for all sonar detection applications and sonar types. To test detection performance, using our own autonomous underwater vehicle, we collected 270 sonar images, which we also share with the community. Compared to the results of benchmark schemes, our detection algorithm shows a trade-off between the probability of detection and the false alarm rate (FAR), which is close to the Kullback-Leibler (KL) divergence lower bound.

The recent boost in undersea operations has led to the development of high-resolution sonar systems mounted on autonomous vehicles. These vehicles are used to scan the seafloor in search of different objects such as sunken ships, archaeological sites, and submerged mines. An important part of the detection operation is the segmentation of sonar images, where the object’s highlight and shadow are distinguished from the seabed background. In this work, we focus on the automatic segmentation of sonar images. We present our enhanced fuzzybased with Kernel metric (EnFK) algorithm for the segmentation of sonar images which, in an attempt to improve segmentation accuracy, introduces two new fuzzy terms of local spatial and statistical information. Our algorithm includes a preliminary de-noising algorithm which, together with the original image, feeds into the segmentation procedure to avoid trapping to local minima and to improve convergence. The result is a segmentation procedure that specifically suits the intensity inhomogeneity and the complex seabed texture of sonar images. We tested our approach using simulated images, real sonar images, and sonar images that we created in two different sea experiments, using multibeam sonar and synthetic aperture sonar. The results show accurate segmentation performance that is far beyond the stateof-the-art results.

We present a non-convex, partially observable solution for underwater vehicle localization, referred to as the time difference of arrival target motion analysis (TD-TMA). TD-TMA is especially suitable for the practical case in which localization ambiguities arise due to a lack of receivers, the effective sound velocity is unknown, and cooperation between the nodes is not available. The method relies on periodic emissions from a target vehicle, which are detected by an observer to yield a series of time difference of arrival (TDoA) measurements. The measurements are arranged in the framework of target motion analysis to obtain a set of localization solutions for the target. Maneuvering of either the target or the observer removes ambiguities and yields a single solution. We formalize TD-TMA as a least squares optimization problem, and solve it in polynomial time. Numerical results show that TD-TMA provides accurate localization performance in terms of both the targets kinematics and positioning, which are close to the posterior Cramer-Rao lower bound. Two sea experiments successfully demonstrate the effectiveness of the solution in real sea environments.