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Joint Information Theoretic and Differential Geometrical Approach for Robust Automated Target Recognition
Final rept. Mar 2009-Feb 2012
FLORIDA UNIV GAINESVILLE DEPT OF ELECTRICAL AND COMPUTER ENGINEERING
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The overall objective of this project is to develop transformative theory and algorithms for robust Automated Target Recognition ATR. This project addressed the following challenging problems in ATR modeling uncertainty, small sample size, high dimensional data, irrelevant featuresdimensions, heterogeneous data, and outliers. In this project, the PI proposed and developed the following new techniques 1 kernel local feature extraction KLFE for ATR applications, 2 technique for identifying network dynamics under sparsity and stationarity constraints, 3 self-organized-queue-based SOQ clustering scheme, 4 robust principal component analysis RPCA based on manifold optimization, outlier detection, and subspace decomposition.
APPROVED FOR PUBLIC RELEASE