Biblio

The Inertial Navigation System(INS) and Doppler Velocity Logs(DVL) which are used frequently on autonomous underwater vehicles can be fused under different types of integration architectures. These architectures differ in terms of algorithm requirements and complexity. DVL may experience acoustic beam losses during operation due to environmental factors and abilities of the sensor. In these situations, radial velocity information cannot be received from lost acoustic beam. In this paper, the performances of INS and DVL integration under tightly and loosely coupled architectures are comparatively presented with simulations. In the tightly coupled approach, navigation filter is updated with solely available beam measurements by using sequential measurement update method, and the sensitivity of this method is investigated for acoustic beam losses.

In view of the challenges with Aids to Navigation (AtoN) managements and emergency response, the present study designs and presents an AtoN real-time video surveillance system based on the AIS collision warning. The key technologies regarding with AtoN cradle head control and testing algorithms, video image fusion, system operation and implementation are demonstrated in details. Case study is performed at Guan River (China) to verify the effectiveness of the AtoN real-time video surveillance system for maritime security supervision. The research results indicate that the intellective level of the AtoN maintenance and managements could be significantly improved. The idea of designing modules brings a good flexibility and a high portability for the present surveillance system, therefore provides a guidance for the design of similar maritime surveillance systems.

Cyber-physical systems (CPSs) encompass a network of sensors and actuators that are monitored, controlled and integrated by a computing and communication core. As autonomous underwater vehicles (AUVs) become more intelligent and connected, new use cases in ocean production, security and environmental monitoring become feasible. Swarms of small, affordable and hydrobatic AUVs can be beneficial in substance cloud tracking and algae farming, and a CPS linking the AUVs with multi-fidelity simulations can improve performance while reducing risks and costs. In this paper, we present a CPS concept tightly linking the AUV network in ROS to virtual validation using Simulink and Gazebo. A robust hardware-software interface using the open-source UAVCAN-ROS bridge is described for enabling hardware-in-the-loop validation. Hardware features of the hydrobatic SAM AUV are described, with a focus on subsystem integration. Results presented include pre-tuning of controllers, validation of mission plans in simulation and real time subsystem performance in tank tests. These first results demonstrate the interconnection between different system elements and offer a proof of concept.

This paper presents netsim, a combined software/hardware system for performing realtime realistic operation of autonomous underwater vehicles (AUVs) with acoustic modem telemetry in a virtual ocean environment. The design of the system is flexible to the choice of physical link hardware, allowing for the system to be tested against existing and new modems. Additionally, the virtual ocean channel simulator is designed to perform in real time by coupling less frequent asynchronous queries to high-fidelity models of the ocean environment and acoustic propagation with frequent pertubation-based updates for the exact position of the simulated AUVs. The results demonstrate the performance of this system using the WHOI Micro-Modem 2 hardware in the virtual ocean environment of the Arctic Beaufort Sea around 73 degrees latitude. The acoustic environment in this area has changed dramatically in recent years due to the changing climate.

Explosive naval mines pose a threat to ocean and sea faring vessels, both military and civilian. This work applies deep neural network (DNN) methods to the problem of detecting minelike objects (MLO) on the seafloor in side-scan sonar imagery. We explored how the DNN depth, memory requirements, calculation requirements, and training data distribution affect detection efficacy. A visualization technique (class activation map) was incorporated that aids a user in interpreting the model's behavior. We found that modest DNN model sizes yielded better accuracy (98%) than very simple DNN models (93%) and a support vector machine (78%). The largest DNN models achieved textless;1% efficacy increase at a cost of a 17x increase of trainable parameter count and computation requirements. In contrast to DNNs popularized for many-class image recognition tasks, the models for this task require far fewer computational resources (0.3% of parameters), and are suitable for embedded use within an autonomous unmanned underwater vehicle.

In this paper, the mathematical framework of behavioral system will be applied to detect the cyber-attack on the networked control system which is used to control the remotely operated underwater vehicle ROV. The Intelligent Generalized Predictive Controller IGPC is used to control the ROV. The IGPC is designed with fault-tolerant ability. In consequence of the used fault accommodation technique, the proposed cyber-attacks detector is able to clearly detect the presence of attacker control signal and to distinguish between the effects of the attacker signal and fault on the plant side. The test result of the suggested method demonstrates that it can be considerably used for detection of the cyber-attack.

Due to the trend of under-ocean exploration, realtime monitoring or long-term surveillance of the under-ocean environment, e.g., real-time monitoring for under-ocean oil drilling, is imperative. Underwater wireless sensor networks could provide an optimal option, and have recently attracted intensive attention from researchers. Nevertheless, terrestrial wireless sensor networks (WSNs) have been well investigated and solved by many approaches that rely on the electromagnetic/optical transmission techniques. Deploying an applicable underwater wireless sensor network is still a big challenge. Due to critical conditions of the underwater environment (e.g., high pressure, high salinity, limited energy etc), the cost of the underwater sensor is significant. The dense sensor deployment is not applicable in the underwater condition. Therefore, Autonomous Underwater Vehicle (AUV) becomes an alternative option for implementing underwater surveillance and target detection. In this article, we present a framework to theoretically analyze the target detection probability in the underwater environment by using AUVs. The experimental results further verify our theoretical results.