Enhancing Situational Awareness to Counter Swarming and Other Nonlinear, Dispersed Tactics Against Naval Surface Forces

Toyon proposes to research and develop algorithms and software that provide enhanced situational awareness to watchstanders of naval vessels. These algorithms will employ advanced data fusion and pattern recognition technologies with the primary objective of identification and early warning of hostile swarming or other nonlinear, dispersed tactics in a 360-degree field-of-regard around the vessel. Fusion algorithms will provide persistent feature-aided tracking, target classification, and target fingerprinting to monitor and characterize each surface vessel in the surveillance region. This set of tracks will supply the data from which group behavioral features will be extracted for subsets of tracks hypothesized to form a coordinated"swarm group". These features will then feed a threat detection algorithm that will detect abnormal behaviors that are observed, such as coordinated actions by a group of targets. In Phase I, Toyon will perform a feasibility study of the proposed solution by developing and extending component data fusion algorithms, designing the group behavioral feature models, and evaluating anomaly detection performance to meet the specified technical objectives of the project.