Topological multimodal sensor data analytics for target recognition and information exploitation in contested environments
Paul Schrader
Abstract
A modern, contested environment produces exponential amounts of data from a vast array of multimodal sensory inputs for intelligence actively updating our situational awareness (SA). The effective management and interpretation of this digital information for (near) real-time decision processes has obscured resulting in imminent costs. This data’s mathematical structures (e.g., its topology) provides a rich, alternative information space where SA could be transformed. Recent successes in topological data analysis (TDA) for a wide array of applications forecast its target recognition capability derived from a sensing grid’s multimodal data and its aggregates. This research introduces novel artificial intelligence/machine learning (AI/ML) pipeline designs invoking TDA-based feature engineering from acoustic, electro-optical (EO), and infrared (IR) data which produce efficient models with near perfect accuracy, precision, and recall in target recognition capability on a range of small unmanned aerial systems (SUASs), ground vehicles, and dismounts (or ground personnel) involving real world environments.