An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> Abstract The current generations of the HUGIN autonomous underwater vehicles (AUVs) have been used in a number of Rapid Environmental Assessment (REA) missions over the past few years, including participation in several national and international military exercises and operations. This article discusses some of the main advantages and limitations of using AUVs in REA operations. This is followed by examples from actual operations that illustrate the choices and trade-offs that must be made. The article aims to demonstrate that AUVs are mature enough for many types of REA operations. <s> BIB001 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> In March and April 2010, an ISE Explorer Autonomous Underwater Vehicle (AUV), built for Natural Resources Canada (NRCan), was deployed to Canada's high Arctic. Its mission was to undertake under-ice bathymetric surveys in support of Canada's submission to establish the outer limits of its continental shelf under the United Nations Convention on the Law of the Sea (UNCLOS). During this deployment several under-ice records were broken and several new technologies were demonstrated. <s> BIB002 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> The power supply for unmanned underwater vehicles has been an important research point since the vehicles were invented. The power systems and environmental energy conversions for the vehicles are reviewed in the paper. Several topics are represented: problems and general solutions for unmanned underwater vehicles power supplies; the mechanisms and structures of tether power system; characteristics of several batteries; the characterization of potential environmental energy, and energy conversion for unmanned underwater vehicles. Docking stations for underwater vehicles continuation are also represented in the paper. Some typical vehicles powered by the power systems and their performances are listed and analyzed. <s> BIB003 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> Autonomous Underwater Vehicles (AUVs) have a wide range of applications in marine geoscience, and are increasingly being used in the scientific, military, commercial, and policy sectors. Their ability to operate autonomously of a host vessel makes them well suited to exploration of extreme environments, from the World’s deepest hydrothermal vents to beneath polar ice sheets. They have revolutionized our ability to image the seafloor, providing higher resolution seafloor mapping data than can be achieved from surface vessels, particularly in deep water. This contribution focuses on the major advances in marine geoscience that have resulted from AUV data. The primary applications are i) submarine volcanism and hydrothermal vent studies, ii) mapping and monitoring of low-temperature fluid escape features and chemosynthetic ecosystems, iii) benthic habitat mapping in shallow- and deep-water environments, and iv) mapping of seafloor morphological features (e.g. bedforms generated beneath ice or sediment-gravity flows). A series of new datasets are presented that highlight the growing versatility of AUVs for marine geoscience studies, including i) multi-frequency acoustic imaging of trawling impacts on deep-water coral mounds, iii) collection of high-resolution seafloor photomosaics at abyssal depths, and iii) velocity measurements of active submarine density flows. Future developments in AUV technology of potential relevance to marine geoscience include new vehicles with enhanced hovering, long endurance, extreme depth, or rapid response capabilities, while development of new sensors will further expand the range of geochemical parameters that can be measured. <s> BIB004 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> This paper presents a data-driven approach to control the movement of autonomous underwater vehicles (AUVs) operating as receivers of a multistatic sonar surveillance network. The algorithm adopts a nonmyopic receding horizon policy to control the AUV heading, with the objective of minimizing the expected estimate error in the target location produced by the onboard tracker. Minimizing this error is of the utmost interest in target state estimation since it is one way of maintaining tracks. The proposed policy builds on an automated perception module which produces a target track and on an acoustic model to estimate the target measurement signal-to-noise ratio. Driven by the mission objectives, it explores the best decisions for the AUV by evaluating the evolution of the tactical situation over a future time window. Nonmyopic approaches can offer increased performance if compared to myopic ones. The downside is an increase of the computational burden. Methods are described to simplify and solve effectively the resulting decision tree to allow the execution of these kinds of computational intensive algorithms onboard autonomous vehicles. At the same time, they aim at producing AUV paths robust to possible misleading target measurements, which might cause meandering trajectories. These are crucial factors to use data-driven strategies for AUVs which must work in real applications. Results from the COLLAB13 sea trial are reported and discussed. They show both the feasibility of executing the algorithm in realtime on an onboard computer and the benefits of using the proposed approach over conventional predefined paths. These results are, to the best of our knowledge, the first successful demonstration at sea of a complex nonmyopic algorithm running in realtime onboard AUVs in a realistic multistatic littoral surveillance scenario. <s> BIB005 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> Abstract Nowadays, underwater acoustic communication is an interesting topic of research. This is especially true in the case of Autonomous Underwater Vehicles (AUVs): due to the limitations of the underwater environment, where WiFi and radio communications are limited, it is very important to rely on efficient and reliable tools for acoustic data exchange. Indeed, an underwater network of acoustic nodes can enhance the navigation and localization capabilities of one or more vehicles. However, cooperation and networking is not feasible in underwater networks composed of vehicles with different software architecture and multi-vendor modems due to the lack of communication standards. In this paper, the authors investigate the use of an AUV as a mobile bridging node within a network of fixed heterogeneous acoustic modems. In particular, MARTA AUV, developed and built by the Department of Industrial Engineering of the University of Florence, is employed to enable data transmission between modems of different manufacturers. The networking and the handling of multiple and multi-vendor modems are instead performed through the SUNSET Software Defined Communication Stack (SDCS) framework, developed by University of Rome La Sapienza jointly with WSENSE Srl. Different bridging tests have been performed in field to evaluate the feasibility of such approach, showing that AUVs can efficiently accomplish the role of gateway between the nodes of the network communicating through the SUNSET SDCS framework. <s> BIB006 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> Abstract In this paper, the target tracking control problem is addressed for underactuated autonomous underwater vehicles (AUV) with a prescribed performance. For this purpose, the range and bearing angles of the AUV relative to an underwater target are transformed to a second-order open-loop error dynamic model by using the prescribed performance bound technique. Then, a new tracking controller is proposed such that the tracking errors converge to an arbitrary small ultimate bound and their transient performance are guaranteed with a pre-specified maximum overshoot and the convergence rate. To overcome unmodeled dynamics and external disturbances that are imposed on the vehicle by the wind, waves, and ocean currents, a multi-layer neural network and an adaptive robust controller are adopted. A Lyapunov stability synthesis shows that all signals of the control system are bounded, and tracking errors converge to a small region containing the origin with a prescribed performance. Finally, simulations are performed in MATLAB software and a comparative study verifies the theoretical results. <s> BIB007 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> In this article, we theoretically investigate the dynamic aspects of the coverage degree and target detection in the underwater environment resulting from the given moving scenarios of the autonomous underwater vehicles (AUVs). With the help of the continuous moving AUVs, the underwater targets, that cannot be detected by the stationary underwater acoustic sensor network, can be detected with an expected probability, which is determined on the basis of the selected moving scenario of the AUVs. We prove that, for an AUV with randomly selected initial starting point and initial direction, the straight trajectory is the optimal route to achieve the maximum coverage and target detection probability. Then, we present a mathematical model to quantitatively analyze the coverage degree in the underwater environment by using AUVs, as well as formulating the target detection probability of both static target detection and mobile target detection cases. Furthermore, by taking the exposure time of the target into account, we mathematically formulate and analyze the impact of the features of an AUV (i.e., sensing range and velocity) and the moving speed of the mobile target to the mobile target detection probability. We carry out intensive simulation experiments to evaluate the proposed mathematical model, and the experimental results further verify the correctness of our theoretical results. <s> BIB008 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> A coherent-noncoherent joint processing framework is proposed for active sonar to combine diversity gain and beamforming gain for detection of a small target in shallow water environments. Sonar utilizes widely-spaced arrays to sense environments and illuminate a target of interest from multiple angles. Meanwhile, it exploits spatial diversity for time-reversal focusing to suppress reverberation, mainly strong bottom reverberation. For enhancement of robustness of time-reversal focusing, an adaptive iterative strategy is utilized in the processing framework. A probing signal is firstly transmitted and echoes of a likely target are utilized as steering vectors for the second transmission. With spatial diversity, target bearing and range are estimated using a broadband signal model. Numerical simulations show that the novel sonar outperforms the traditional phased-array sonar due to benefits of spatial diversity. The effectiveness of the proposed framework has been validated by localization of a small target in at-lake experiments. <s> BIB009 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> Autonomous underwater vehicles (AUVs) are practical tools for ocean observation. However, they tend to operate in an automatic rather than autonomous way. This reflects the attitudes and behaviors that individuals and organizations share when adopting new technology in this industry. This paper clarifies the factors that are preventing one important aspect of autonomy—adaptive mission planning (AMP)—from transitioning from research to commercial and bespoke AUVs. A total of 25 experts comprising AUV developers and users, with combined 237 years of experience, provided their views in a structured survey covering several different hypotheses. There is insufficient evidence to determine clearly a single reason for failure to adopt AMPs, but a primary cause is the paucity of demonstration trials. This view is irrespective of participants' years of experience. Managers, engineers, and technologists agree on the two most likely causes for failure to adopt AMPs. However, the differences between the assessments provided by researchers and these three professional groups are statistically significant, with p value <0.005. For researchers, complexity is one of the two most important inhibitory factors. We present recommendations to support the integration of AMP into AUVs substantiated by recent examples where government, industry, and researchers have developed and tested AMPs. <s> BIB010 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> In this paper we present a novel method for the acoustic tracking of multiple Autonomous Underwater Vehicles. While the problem of tracking a single moving vehicle has been addressed in the literature, tracking multiple vehicles is a problem that has been overlooked, mostly due to the inherent difficulties on data association with traditional acoustic localization networks. The proposed approach is based on a Probability Hypothesis Density Filter, thus overcoming the data association problem. Our tracker is able not only to successfully estimate the positions of the vehicles, but also their velocities. Moreover, the tracker estimates are labelled, thus providing a way to establish track continuity of the targets. Using real word data, our method is experimentally validated and the performance of the tracker is evaluated. <s> BIB011 </s> An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture <s> A. AUTONOMOUS UNDERWATER VEHICLES (AUVs) <s> 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 range-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. <s> BIB012
AUVs seem to be a practical device for monitoring, observing and inspecting the ocean. These are autonomous vehicles that propel themselves through the water for durations of few hours to numerous days without the need of their dropping vessels. AUVs move at the speeds of almost 1.5 to 2.0 m/s on fixed trajectories. They are battery-powered vehicles with high energy density. However, high cost is required to provide enough energy for the completion of the whole mission, as duration of batteries is limited. Due to these batteries, AUVs have light-weight power sources where docking stations are required to recharge the batteries BIB003 . AUVs need less turning time to make 180-degree shift and may provide covert and silent operations such as mine hunting BIB001 . There are also some operative and deployable limitations that do not allow AUVs to move in areas of high military, shipping, or fishing activity BIB004 . Nevertheless, to perform several underwater tasks in a fully automated mode, AUVs are emerging as an efficient and reliable solution for various underwater complexities. The development of AUVs was started in 1990s at the research centers for gathering data and performing tasks that could not have been done in any other way BIB002 . To track the movement of AUVs working as receivers for multi-static sonar surveillance network, a data-driven nonmyopic approach has been proposed in BIB005 . The objective of the algorithm was to minimize the expected estimate error generated by the onboard tracker while locating the target. Error minimization is significant in state estimation of targets for maintaining tracks. The algorithm performs better for making decision about AUV by estimating tactical situation with a drawback of an increase in computational overhead. The results are the first successful demonstration at sea to control real-time movement of AUVs in a realistic surveillance scenario. However, a framework to deal with multiple tracks depending on the growing tactical scenarios is yet to be developed. A total of 25 experts that are part of development in adaptive mission planning (AMP) have reviewed a survey to find the answer why AUVs have not yet been deployed for AMP BIB010 . It was difficult for them to find clearly a single cause for failure to implement AMP; however, the main reason is the lack of demonstrations. The authors in BIB006 used AUV to bridge the gap that hinders cooperation among vehicles of different architectures and modems. An AUV developed and built by the Department of Industrial Engineering of the University of Florence, MARTA AUV was particularly used to allow cooperation and data transmission among vehicles of multi-vendors modems. The experiments conducted with the help of SUNSET Software Defined Communication Stack (SDCS) framework (University of Rome La Sapienza) that opens doors for complex networking scenarios where heterogenous vehicles and systems are deployed. Another scheme proposed in BIB007 to solve the target tracking problem of AUVs in 3D space considered three-layer neural network and an adaptive robust controller to overcome the hindrances caused by wind, waves, and other environmental factors. The scheme assumes that AUVs are equipped with range and heading sensors, however for real implementation these sensors are of vital importance. Among several algorithms that exist in literature to track single vehicle, fleets of AUVs are also used to achieve the same goal. A novel method BIB011 for the tracking of multiple AUVs is based on Probability Hypothesis Density (PHD) filter to predict the position as well as velocities of the vehicles. This is the first AUV tracker that tracks multiple vehicles in real scenarios and its validity has been approved through successful experiments. However, the scheme is limited to only underwater robotics. A passive method known as Reverse Bearing Only Target Motion Analysis (Reverse BO-TMA) for the self-localization of AUV is presented in BIB012 that allows AUV to maintain its distance from the source vehicle without requiring any cooperation. The accuracy of the method has been validated both numerically and experimentally; however, the method does not support inertial navigation system of AUVs. Another mathematical model is presented in BIB008 to analyze the probability of detection of mobile target by using AUVs. The method uses a passive sonar system to analyze the features of AUV. Likewise, for active sonar, a coherentnoncoherent joint processing framework is proposed in BIB009 to detect a small target in shallow water.