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.

We present a non-linear navigation solution, referred to as the Reverse Bearing Only Target Motion Analysis (Reverse BO-TMA). Reverse BO-TMA is a passive method for the self-localization of an Autonomous Underwater Vehicle (AUV). Our method relies solely on bearing measurements based on the radiated noise of a passing vessel sailing along a known route. Compared to traditional ange-based underwater localization methods, Reverse BO-TMA allows the AUV to remain farther from the reference vessel, and does not require collaboration or message exchange. We formalize the Reverse BO-TMA as an optimization problem, and offer both a least squares solution and an unscented Kalman filter solution. Numerical results show that Reverse BO-TMA provides accurate performance, in terms of both positioning and speed, which are close to the posterior Cramer-Rao lower bound. To demonstrate the effectiveness of our approach, we have implemented a prototype for Reverse BO-TMA and successfully tested it in three sea experiments. We show that the Reverse BO-TMA is suitable for the long-term deployment of an AUV and in cases where energy is scarce and cooperating anchors are not available.

The rise in shipborne underwater radiated noise (URN) has been recognized as a form of pollution that necessitates regular monitoring. Current monitoring procedures require vessel cooperation and costly infrastructure. We identify a knowledge gap regarding the monitoring of URN from vessels of opportunity, where recordings may capture noise from multiple vessels simultaneously. To address this, we propose a framework that uses clustering techniques to differentiate between narrowband tonal components produced by various vessels, as recorded by a single omnidirectional hydrophone. These tonal lines are then associated with nearby ships using data from their automatic identification system (AIS). Our approach involves feature extraction, allowing for the classification of tonal lines into clusters that likely originate from the same vessel. These clusters are then matched with AIS tracks based on their temporal correlation. Our method eliminates the need for manual intervention for vessel screening and tagging. The methodology was tested using data collected from a recorder placed near the approach route to the Haifa port in Israel. The results demonstrate over high accuracy in tonal clustering and above 99% true positive in cluster–AIS associations.

Underwater acoustic technologies are a key component for exploring the behavior of marine fauna such as sea turtles, sharks, and seals. The animals are marked with acoustic devices (tags) that periodically emit signals encoding the device’s ID along with sensor data such as depth, temperature, or the dominant acceleration axis - data that is collected by a network of deployed receivers. In this work, we aim to optimize the locations of receivers for best tracking of acoustically tagged marine fauna, and provide a test case with tags suitable for megafauna tracking. The outcomes of such tracking allows the evaluation of the animals’ motion patterns, their hours of activity, and their social interactions. In particular, we focus on how to determine the receivers’ deployment positions to maximize the coverage area in which the tagged animals can be tracked. For example, an overly-condensed deployment may not allow accurate tracking, whereas a sparse one, may lead to a small coverage area due to too few detections. We formalize the question of where to best deploy the receivers as a non-convex constraint optimization problem that takes into account the local environment and the specifications of the tags, and offer a sub-optimal, low-complexity solution that can be applied to large testbeds. Numerical investigation for three stimulated sea environments shows that our proposed method is able to increase the localization coverage area by 30%, and results from a test case experiment demonstrate similar performance in a real sea environment. We share the implementation of our work to help researchers set up their own acoustic observatory.

The eastern Mediterranean Sea is among the warmest and most oligotrophic marine regions globally. Shark aggregations at coastal infrastructure in this basi