In Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX (Vol. 10635, p. 1063513), International Society for Optics and Photonics (May 2018).
The ability to collect an ever-increasing amount of information is outpacing analysts’ ability to interpret and communicate that information in a timely manner. The Army Research Laboratory’s (ARL’s) Signal and Image Processing (SIP) Division is presently engaged in research to develop a system-of-systems methodology designed around a Mission & Means Framework (MMF), a robust, rapid-reaction, autonomous information-generating tool that can provide the mission-relevant information/intelligence that Commanders need to make winning decisions on the battlefield. The MMF provides a structure that enables the optimal allocation of available information sources to capture and exploit Mission-Informed Needed Information based on Discoverable, Available Sensing Sources (MINI-DASS). In this paper, we describe an MMF operator that matches information needs to information means using ontologies that describe both the information requirements and the information sources. We then describe two different multi-objective optimization techniques to effectively explore the large, complex search space of possible matches to discover suitable solutions that match available information-source means to satisfy mission needs.
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