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14 May 2019 Testing active polarimetric imagers in fog (Conference Presentation)
John D. van der Laan, Brian J. Redman, Jacob W. Segal, Karl Westlake, Jeremy B. Wright
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Proceedings Volume 11019, Situation Awareness in Degraded Environments 2019; 1101909 (2019) https://doi.org/10.1117/12.2519122
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States
Abstract
Heavy fogs and other highly scattering environments pose a challenge for many commercial and national security sensing systems. Current autonomous systems rely on a range of optical sensors for guidance and remote sensing that can be degraded by highly scattering environments. In our previous and on-going simulation work, we have shown polarized light can increase signal or range through a scattering environment such as fog. Specifically, we have shown circularly polarized light maintains its polarized signal through a larger number of scattering events and thus range, better than linearly polarized light. In this work we present design and testing results of active polarization imagers at short-wave infrared and visible wavelengths. We explore multiple polarimetric configurations for the imager, focusing on linear and circular polarization states. Testing of the imager was performed in the Sandia Fog Facility. The Sandia Fog Facility is a 180 ft. by 10 ft. chamber that can create fog-like conditions for optical testing. This facility offers a repeatable fog scattering environment ideally suited to test the imager’s performance in fog conditions. We show that circular polarized imagers can penetrate fog better than linear polarized imagers.
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John D. van der Laan, Brian J. Redman, Jacob W. Segal, Karl Westlake, and Jeremy B. Wright "Testing active polarimetric imagers in fog (Conference Presentation)", Proc. SPIE 11019, Situation Awareness in Degraded Environments 2019, 1101909 (14 May 2019); https://doi.org/10.1117/12.2519122
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KEYWORDS
Fiber optic gyroscopes
Imaging systems
Polarimetry
Scattering
Light scattering
Environmental sensing
Fourier transforms
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