Battle Damage Assessment Information Fusion

The operational commander must interpret large amounts of real-time, imperfect data from multiple heterogeneous sources in order to perform Battle Damage Assessment (BDA). The challenge is to develop a semi-automated BDA system that will provide the commander with accurate, relevant and timely information allowing him/her to focus on the relevant usable action-oriented knowledge extracted from the incoming data stream. The goal of this research is to address this challenge by building on the JDL data fusion model. This innovative BDA software prototype will incorporate a suite of intelligent agents capable of performing natural language processing, ontological processing, and data mining. Our goals will be realized through two major activities: (1) researching and adapting known and novel data fusion methodologies, and (2) developing a collaborative intelligent agent BDA (IABDA) software prototype that implements the DNN architecture to control data fusion. The IABDA software will support the operational commanders in monitoring and estimating the complete battle damage picture, supplementing their creative and cognitive abilities with automated techniques. Ultimately, this will approach will lead to improved efficiency and operational effectiveness.