Self-proficiency assessment for ATR systems

Erik Blasch

doi:10.1117/12.2563259

19 May 2020 Self-proficiency assessment for ATR systems

Erik Blasch

Proceedings Volume 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII; 113930T (2020) https://doi.org/10.1117/12.2563259
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Usability of automatic target recognition (ATR) systems requires performance evaluation with measures of performance (MOPs) and measures of effectiveness (MOEs). MOEs support an external proficiency review, while there is a need for internal MOEs which are a form of self-proficiency. Self-proficiency of people is well known for job selection, performance, and productivity. Likewise, there is a need for self-proficiency determination of emerging data analytics techniques such as from artificial intelligence, machine learning (ML), signal processing, and data fusion. The coordination of machines with humans for autonomy requires external and internal proficiency assessment. In this paper, a discussion on self-proficiency assessment for ATR analysis is provided to enhance human and machine awareness and performance assessment. An example comes from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set with human-machine proficiency analysis.

Citation Download Citation

Erik Blasch "Self-proficiency assessment for ATR systems", Proc. SPIE 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII, 113930T (19 May 2020); https://doi.org/10.1117/12.2563259

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