Multi-sensor synthetic data generation for performance characterization
Christopher R. Paulson, Adam R. Nolan, Lori A. Westerkamp, Edmund G. Zelnio · 2019
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.