A performance modeling framework for large scale synthetically derived performance estimates

G. Steven Goley; Brian Thelen; Ismael Xique; Adam R. Nolan

doi:10.1117/12.2523455

14 May 2019 A performance modeling framework for large scale synthetically derived performance estimates

G. Steven Goley, Brian Thelen, Ismael Xique, Adam R. Nolan

Proceedings Volume 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI; 109870K (2019) https://doi.org/10.1117/12.2523455
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

As the Air Force pushes toward reliance on autonomous systems for navigation, situational awareness, threat analysis and target engagement there are several requisite technologies that must be developed. Key among these is the concept of `trust' in the autonomous system to perform its task. This term, `trust' has many application specific definitions. We propose that a properly calibrated algorithm confidence is essential to establishing trust. To accomplish properly calibrated confidence we present a framework for assessing algorithm performance and estimating confidence of a classifier's declaration. This framework has applications to improved algorithm trust, fusion, and diagnostics. We present a metric for comparing the quality of performance modeling and examine three different implementations of performance models on a synthetic dataset over a variety of operating conditions.

Conference Presentation

Citation Download Citation

G. Steven Goley, Brian Thelen, Ismael Xique, and Adam R. Nolan "A performance modeling framework for large scale synthetically derived performance estimates", Proc. SPIE 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI, 109870K (14 May 2019); https://doi.org/10.1117/12.2523455

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