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Proceedings Paper

Automatic machine learning for target recognition
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Paper Abstract

Automatic Target Recognition (ATR) seeks to improve upon techniques from signal processing, pattern recognition (PR), and information fusion. Currently, there is interest to extend traditional ATR methods by employing Artificial Intelligence (AI) and Machine Learning (ML). In support of current opportunities, the paper discusses a methodology entitled: Systems Experimentation efficiency effectives Evaluation Networks (SEeeEN). ATR differs from PR in that ATR is a system deployment leveraging pattern recognition (PR) in a networked environment for mission decision making, while PR/ML is a statistical representation of patterns for classification. ATR analysis has long been part of the COMPrehensive Assessment of Sensor Exploitation (COMPASE) Center utilizing measures of performance (e.g., efficiency) and measures of effectiveness (e.g., robustness) for ATR evaluation. The paper highlights available multimodal data sets for Automated ML Target Recognition (AMLTR).

Paper Details

Date Published: 14 May 2019
PDF: 12 pages
Proc. SPIE 10988, Automatic Target Recognition XXIX, 109880L (14 May 2019); doi: 10.1117/12.2519221
Show Author Affiliations
Erik Blasch, Air Force Research Lab. (United States)
Uttam K. Majumder, Air Force Research Lab. (United States)
Todd Rovito, Air Force Research Lab. (United States)
Peter Zulch, Air Force Research Lab. (United States)
Vincent J. Velten, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 10988:
Automatic Target Recognition XXIX
Riad I. Hammoud; Timothy L. Overman, Editor(s)

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