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Proceedings Paper

Multi-sensor synthetic data generation for performance characterization
Author(s): Christopher Paulson; Adam Nolan; Lori Westerkamp; Edmund Zelnio
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Paper Abstract

This paper introduces an innovative framework for the development of multi-sensor datasets for target recognition. This framework goes beyond the paradigm of generating synthetic data to augment algorithm training; it employs carefully generated training and test data to characterize algorithm performance over any desired operating conditions, culminating in the ability to generate algorithm performance models for use in fusion, sensor resource management, and mission simulation. The current system instantiates the full path, from operating conditions to synthetic data to results, for synthetic aperture radar. Fully integrated electro-optic and laser radar paths, to be completed in 2019, will comprise a complete multi-sensor testbed for performance prediction. Future work will add sensor modes as well as automated decision and feature fusion for target identification.

Paper Details

Date Published: 14 May 2019
PDF: 10 pages
Proc. SPIE 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI, 1098707 (14 May 2019); doi: 10.1117/12.2523579
Show Author Affiliations
Christopher Paulson, Air Force Research Lab. (United States)
Adam Nolan, Etegent Technologies, Ltd. (United States)
Lori Westerkamp, Air Force Research Lab. (United States)
Edmund Zelnio, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 10987:
Algorithms for Synthetic Aperture Radar Imagery XXVI
Edmund Zelnio; Frederick D. Garber, Editor(s)

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