Paper
29 May 2014 DARPA super resolution vision system (SRVS) robust turbulence data collection and analysis
Author Affiliations +
Abstract
Atmospheric turbulence degrades the range performance of military imaging systems, specifically those intended for long range, ground-to-ground target identification. The recent Defense Advanced Research Projects Agency (DARPA) Super Resolution Vision System (SRVS) program developed novel post-processing system components to mitigate turbulence effects on visible and infrared sensor systems. As part of the program, the US Army RDECOM CERDEC NVESD and the US Army Research Laboratory Computational & Information Sciences Directorate (CISD) collaborated on a field collection and atmospheric characterization of a two-handed weapon identification dataset through a diurnal cycle for a variety of ranges and sensor systems. The robust dataset is useful in developing new models and simulations of turbulence, as well for providing as a standard baseline for comparison of sensor systems in the presence of turbulence degradation and mitigation. In this paper, we describe the field collection and atmospheric characterization and present the robust dataset to the defense, sensing, and security community. In addition, we present an expanded model validation of turbulence degradation using the field collected video sequences.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Richard L. Espinola, Kevin R. Leonard, Roger Thompson, David Tofsted, and Sean D'Arcy "DARPA super resolution vision system (SRVS) robust turbulence data collection and analysis", Proc. SPIE 9071, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXV, 90711A (29 May 2014); https://doi.org/10.1117/12.2053473
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Cited by 2 scholarly publications.
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KEYWORDS
Turbulence

Sensors

Spatial frequencies

Systems modeling

Modulation transfer functions

Analytical research

Atmospheric sensing

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