From Conventional to Machine Learning Methods for Maritime Risk Assessment

Within the last thirty years, the range and complexity of methodologies proposed to assess maritime risk have increased significantly. Techniques such as expert judgement, incident analysis, geometric models, domain analysis and Bayesian Networks amongst many others have become dominant within both the literature and industry. On top of this, advances in machine learning algorithms and big data have opened opportunities for new methods which might overcome some limitations of conventional approaches. Yet, determining the suitability or validity of one technique over another is challenging as it requires a systematic multicriteria approach to compare the inputs, assumptions, methodologies and results of each method. Within this paper, such an approach is proposed and tested within an isolated waterway in order to justify the proposed advantages of a machine learning approach to maritime risk assessment and should serve as inspiration for future work

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Virtual aids to navigation

There are many examples of master, bridge crew and pilot errors in navigation causing grounding under adverse circumstances that were known and published in official notices and records. Also dangerous are hazards to navigation resulting from dynamic changes within the marine environment, inadequate surveys and charts. This research attempts to reduce grounding and allision incidents and increase safety of navigation by expanding mariner situational awareness at and below the waterline using new technology and developing methods for the creation, implementation and display of Virtual Aids to Navigation (AtoN) and related navigation information. This approach has widespread significance beyond commonly encountered navigation situations. Increased vessel navigation activity in the Arctic and sub-Arctic regions engenders risk due, in part, to the inability to place navigational aids and buoys in constantly changing ice conditions. Similar conditions exist in tropical regions where sinker placement to moor buoys in sensitive environmental areas with coral reefs is problematic. Underdeveloped regions also lack assets and infrastructure needed to provide adequate navigation services, and infrastructure can also rapidly perish in developed regions during times of war and natural disaster. This research exploits rapidly developing advances in environmental sensing technology, evolving capabilities and improved methods for reporting real time environmental data that can substantially expand electronic navigation aid availability and improve knowledge of undersea terrain and imminent hazards to navigation that may adversely affect ship operations. This is most needed in areas where physical aids to navigation are scarce or non-existent as well as in areas where vessel traffic is congested. Research to expand related vessel capabilities is accomplished to overcome limitations in existing and planned electronic aids, expanding global capabilities and resources at relatively low-cost. New methods for sensor fusion are also explored to vi reduce overall complexity and improve integration with other navigation systems with the goal of simplifying navigation tasks. An additional goal is to supplement training program content by expanding technical resources and capabilities within the confines of existing International Convention on Standards for Training, Certification and Watchkeeping for Seafarers (STCW) requirements, while improving safety by providing new techniques to enhance situational awareness

Annual General Assembly of the International Association of Maritime Universities

978-84-947311-7-

レーダー画像からの自動航跡取得性能の改善と東京湾海上交通流の解析に関する研究

東京海洋大学修士学位論文 平成20年度(2008) 海運ロジスティクス 第809号指導教員: 田丸人意全文公表年月日: 2009-05-07東京海洋大学200

Machine Learning for Enhanced Maritime Situation Awareness: Leveraging Historical AIS Data for Ship Trajectory Prediction

In this thesis, methods to support high level situation awareness in ship navigators through appropriate automation are investigated. Situation awareness relates to the perception of the environment (level 1), comprehension of the situation (level 2), and projection of future dynamics (level 3). Ship navigators likely conduct mental simulations of future ship traffic (level 3 projections), that facilitate proactive collision avoidance actions. Such actions may include minor speed and/or heading alterations that can prevent future close-encounter situations from arising, enhancing the overall safety of maritime operations. Currently, there is limited automation support for level 3 projections, where the most common approaches utilize linear predictions based on constant speed and course values. Such approaches, however, are not capable of predicting more complex ship behavior. Ship navigators likely facilitate such predictions by developing models for level 3 situation awareness through experience. It is, therefore, suggested in this thesis to develop methods that emulate the development of high level human situation awareness. This is facilitated by leveraging machine learning, where navigational experience is artificially represented by historical AIS data. First, methods are developed to emulate human situation awareness by developing categorization functions. In this manner, historical ship behavior is categorized to reflect distinct patterns. To facilitate this, machine learning is leveraged to generate meaningful representations of historical AIS trajectories, and discover clusters of specific behavior. Second, methods are developed to facilitate pattern matching of an observed trajectory segment to clusters of historical ship behavior. Finally, the research in this thesis presents methods to predict future ship behavior with respect to a given cluster. Such predictions are, furthermore, on a scale intended to support proactive collision avoidance actions. Two main approaches are used to facilitate these functions. The first utilizes eigendecomposition-based approaches via locally extracted AIS trajectory segments. Anomaly detection is also facilitated via this approach in support of the outlined functions. The second utilizes deep learning-based approaches applied to regionally extracted trajectories. Both approaches are found to be successful in discovering clusters of specific ship behavior in relevant data sets, classifying a trajectory segment to a given cluster or clusters, as well as predicting the future behavior. Furthermore, the local ship behavior techniques can be trained to facilitate live predictions. The deep learning-based techniques, however, require significantly more training time. These models will, therefore, need to be pre-trained. Once trained, however, the deep learning models will facilitate almost instantaneous predictions

ECHO Information sharing models

As part of the ECHO project, the Early Warning System (EWS) is one of four technologies under development. The E-EWS will provide the capability to share information to provide up to date information to all constituents involved in the E-EWS. The development of the E-EWS will be rooted in a comprehensive review of information sharing and trust models from within the cyber domain as well as models from other domains

Unmanned Aircraft Systems in the Cyber Domain

Unmanned Aircraft Systems are an integral part of the US national critical infrastructure. The authors have endeavored to bring a breadth and quality of information to the reader that is unparalleled in the unclassified sphere. This textbook will fully immerse and engage the reader / student in the cyber-security considerations of this rapidly emerging technology that we know as unmanned aircraft systems (UAS). The first edition topics covered National Airspace (NAS) policy issues, information security (INFOSEC), UAS vulnerabilities in key systems (Sense and Avoid / SCADA), navigation and collision avoidance systems, stealth design, intelligence, surveillance and reconnaissance (ISR) platforms; weapons systems security; electronic warfare considerations; data-links, jamming, operational vulnerabilities and still-emerging political scenarios that affect US military / commercial decisions. This second edition discusses state-of-the-art technology issues facing US UAS designers. It focuses on counter unmanned aircraft systems (C-UAS) – especially research designed to mitigate and terminate threats by SWARMS. Topics include high-altitude platforms (HAPS) for wireless communications; C-UAS and large scale threats; acoustic countermeasures against SWARMS and building an Identify Friend or Foe (IFF) acoustic library; updates to the legal / regulatory landscape; UAS proliferation along the Chinese New Silk Road Sea / Land routes; and ethics in this new age of autonomous systems and artificial intelligence (AI).https://newprairiepress.org/ebooks/1027/thumbnail.jp

Game Assessment For Miltary Application

The primary purpose of conducting this research was to establish game assessment guidelines and characteristics for integrating elected characteristics of games into ongoing instructional approaches. The cost of repurposing commercial-off-the-shelf (COTS) games could offer a considerably lower cost alternative than the cost of creating a new instructional game developed for a specific instructional goal. The McNeese Game Assessment Tool (MGAT), created for the assessment of games in this usability study, is currently in a beta stage and was found to have potential for future game assessment. The overall assessment indicated that the tool was effective in analyzing game products for reuse potential and that the five instruments that make up the tool did meet the purpose of the design. However, the study also indicated that the instruments needed recommended modifications and further testing with a larger population group before the tool could be utilized. The assessment process identified in this study was a step forward in the area of game and simulation integration research. This study indicated that more research is needed in the area of instructional design to enhance instructional integration goals for future game, simulation and training applications