An integrated maritime reasoning and monitoring system

Maritime information systems typically offer system features for data collection, information fusion and the automated construction of a basic recognized maritime picture for human operators. However making sense of maritime situations is still very much an intensive human-based cognitive endeavor. Limited human resources face difficult cognitive challenges in making sense of huge amount of data generated daily by dynamic round-the-clock maritime shipping and port activities. An effective information exploitation system for maritime surveillance and monitoring is expected to be able to augment human-based surveillance and monitoring operations with machine-based computing capabilities to interpret and reason about massive amount of situational information, as well as transforming this knowledge into actionable decision indicators for the human decision makers. The Defence Science & Technology Agency (DSTA) has developed the PACKED model to guide the exploration and development of computing technologies that support human decision making and other cognitive challenges in information rich, complex, and dynamic operational environments. An integrated maritime reasoning and monitoring system illustrates how computing technologies are applied in the areas of Attention, Knowledge, and Comprehension to support situation analysis and monitoring processes for maritime surveillance. The maritime reasoning and monitoring system consist of Bayesian reasoning, entity network analysis, and movement pattern analysis, to integrate, analyze, interpret and reason about maritime data. The Graylist Network Analysis (GNA) engine probes networks of entity-relationships to uncover “graylisted” entities that are closely associated with blacklisted entities. The Movement Pattern Analysis (MPA) engine uncovers common patterns in vessel movements and monitors real-time for maritime traffic data for anomalous vessel movements. The Bayesian network inference engine combine- situation information from data sources and analysis results produced by analytical engines such as the GNA and MPA, with Bayesian knowledge models in order to infer probabilities of occurrences of maritime scenarios.