Automatic machine learning for target recognition

Erik Blasch; Uttam K. Majumder; Todd Rovito; Peter Zulch; Vincent J. Velten

doi:10.1117/12.2519221

14 May 2019 Automatic machine learning for target recognition

Erik Blasch, Uttam K. Majumder, Todd Rovito, Peter Zulch, Vincent J. Velten

Proceedings Volume 10988, Automatic Target Recognition XXIX; 109880L (2019) https://doi.org/10.1117/12.2519221
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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).

Conference Presentation

Citation Download Citation

Erik Blasch, Uttam K. Majumder, Todd Rovito, Peter Zulch, and Vincent J. Velten "Automatic machine learning for target recognition", Proc. SPIE 10988, Automatic Target Recognition XXIX, 109880L (14 May 2019); https://doi.org/10.1117/12.2519221

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