Soft Data-Augmented Risk Assessment and Automated Course of Action Generation for Maritime Situational Awareness

Abstract

This thesis presents a framework capable of integrating hard (physics-based) and soft (people-generated) data for the purpose of achieving increased situational assessment (SA) and effective course of action (CoA) generation upon risk identification. The proposed methodology is realized through the extension of an existing Risk Management Framework (RMF). In this work, the RMF’s SA capabilities are augmented via the injection of soft data features into its risk modeling; the performance of these capabilities is evaluated via a newly-proposed risk-centric information fusion effectiveness metric. The framework’s CoA generation capabilities are also extended through the inclusion of people-generated data, capturing important subject matter expertise and providing mission-specific requirements. Furthermore, this work introduces a variety of CoA-related performance measures, used to assess the fitness of each individual potential CoA, as well as to quantify the overall chance of mission success improvement brought about by the inclusion of soft data. This conceptualization is validated via experimental analysis performed on a combination of real- world and synthetically-generated maritime scenarios. It is envisioned that the capabilities put forth herein will take part in a greater system, capable of ingesting and seamlessly integrating vast amounts of heterogeneous data, with the intent of providing accurate and timely situational updates, as well as assisting in operational decision making.