Information-Fusion based Indication & Warning Assessment and Recognition System (IIWARS)

We propose to design, develop, and validate an Information-Fusion based Indication & Warning Assessment and Recognition System (IIWARS) for assessing and discovering asymmetric threats. The proposed approach combines text mining and machine learning techniques with information fusion to facilitate dynamic and adaptive threat monitoring and prediction. The main product will be a an application-hardened IIWARS software system that will facilitates the automated generation of threat information from distributed, multi-source, text data to support early detection of emerging asymmetric threats. IIWARS innovative methods include text and data mining for asymmetric threat assessment, information fusion, and hybrid threat learning using machine learning and ontology-assisted methods. Building off the threat assessment and threat learning capabilities developed and demonstrated in the successful Phase I project, the proposed Phase II project will (i) refine and enhance the IIWARS threat assessment methods, (ii) refine and enhance the IIWARS threat learning methods, (iii) develop and test a focused IIWARS homeland security asymmetric threat assessment application, and (iv) generalize and transition IIWARS for large scale Air Force, DoD, and commercial applications. We anticipate rapid transition of the IIWARS technology innovations into ongoing and planned advanced technology development and demonstration initiatives at the Rome Air Force Research Laboratory.