Discovering anomalies in land, sea, air, and space is a critical task for Situational Awareness activities. It is also an important enabler for maritime and airspace security operations. Artificial Intelligence and Machine Learning (AI/ML) algorithms can be applied to learn the regular behavior, track targets of interests, and identify anomalies in these domains. Specifically, unsupervised learning techniques can be applied to large dataset (without labels) to learn the normal traffic behavior and extract contextual information as a base to learn patterns of life. University Technical Services, Inc. (UTS) along with its subcontractor University Research Foundation (URF) proposes to design and implement the Hierarchical Artificial Intelligence-Based Algorithms for Target Identification & Tracking and Anomaly Detection in Congested Environments (HABIT) software solution. Approved for Public Release | 22-MDA-11215 (27 Jul 22)
