Analysis and Characterization of Pattern Classifiers; GASP - Generator of Adaptive Statistical Pattern Recognition Systems

摘要

Report developed under STTR Contract for topic ARMY 02-T004 Under this effort, Frontier Technology, Inc. (FTI) and University of Florida (UF) are developing designs for automatically-generated statistical pattern recognition systems (GASPs) that can classify uncooperative targets among time-varying natural and manmade backgrounds. We also propose to analyze the performance of the envisioned GASPs to: (a) covertly acquire feature data (e.g., statistical, spectral, and spatial cues) from target/background imagery, (b) apply multiple classifiers to target background information to select probable target location and identity, (c) apply inferencing rules to disambiguate infeasible or contradictory classifier outputs. Pattern selection, key to successful system operation in mission- and threat-specific scenarios, will utilize Dempster- Schaefer theory and UF's powerful data fusion paradigm, Morphological Neural Nets (MNN). Phase-I will evaluate, extend and exploit FTI and UF's successful, DoD-sponsored R&D for dynamic pattern recognition and ATR to develop and test an efficient system design for target classifier output fusion and disambiguation. System design will include analysis of complexity and cost of potential hardware implementations. In a Phase-II effort, we will use Phase-I results to drive candidate pattern downselection in FTI's DoD-supported TNE paradigm. MNNs and TNE have been proven highly successful in a wide variety of recognition problems, thus we propose to analyze GASP system performance in realistic ATR scenarios.