Given the dynamic nature of battlespace systems, testing for a broad range of contingencies is extremely challenging. Better models of large-scale systems are needed to allow virtual testing and analysis.
Continuing research will be required to find multilevel representations of multimodal signal sets so that they can be efficiently stored, transmitted, manipulated, and understood by digital techniques. This means that basic digital signal processing topics such as signal enhancement, signal modeling, redundancy removal, and feature extraction will continue to be important fundamental research topics. However, the special nature of defense applications argues for continued support by AFOSR of work that leverages and extends signal-processing R&D of a more general nature.
There is also a need for research on multimedia data mining and machine learning in the defense context. Research on basic signal representation and research on signal analysis must be closely intertwined. Combining information across different signal modes and fusing that information into robust multimedia “documents” with high semantic value and focused on information sources of interest in Air Force applications will allow AFOSR to leverage non-defense-based research and foster the creation of new knowledge with direct applicability to Air Force problems.