A Hierarchical Data Fusion Architecture for Missile Detection and Identification
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AbstractGlobal Technology Connection, Inc., in collaboration with Center for Multisource Information Fusion (CMIF) at SUNY (Buffalo) and Boeing Net-Centric Operations, proposes the development of data fusion architecture based on a hybrid analytical / intelligent methodology that exploits the concept of "focus of attention" via active perception in order to optimize missile classification accuracy while reducing substantially the computational burden. The fusion scheme incorporates several levels of abstraction: fusion at the data level, the feature level and the sensor level. The overall architecture employs technologies from soft computing, Dempster-Shafer theory and game theory to provide a robust and reliable platform for critical aerospace systems. Phase I effort will develop and test the feasibility of the data fusion algorithms for missile detection and identification using the Distributed Data Fusion (DDF) testbed. The DDF simulator was developed by SUNY (Buffalo) to test and evaluate algorithms to perform networking, sensor simulation, target tracking and fusion in a battlefield environment. Phase II will address design and construction of prototype for implementing the data fusion concept for components that includes logic for dynamic topology, management of features, a variety of sensor platforms, 3D visualization of threat objects, etc. and a performance evaluation module. Several aerospace end users like Boeing Phantom Works have already expressed interest in the commercial applications (Phase III) of this approach for their missile defense systems.
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