Abstract

Light experiences scattering and absorption events in aerosols like fog that depend on the size and refractive index of the particles that the light interacts with. Information becomes scrambled and difficult to decipher such that naturally occurring and man-made aerosols can impact astronomy, transportation, aviation, remote sensing, security, and more. To improve the situation, we have developed a computational sensing modality using a new approximate integral equation solution to the radiative transfer equation. To accomplish this, first, an efficient analytic solution was found that accounts for the effect of an object within the scattering media and predicts measurements by a camera’s pixel array. This model appears to be applicable in both the moderate and highly scattering regimes, thereby covering the applicability domain of both the small angle and diffusion approximations. Next, we employed a statistical approach that leverages the model to detect and localize opaque objects hidden in fog. Detection was accomplished with a binary hypothesis test and the Neyman-Pearson lemma, and localization was achieved using the maximum likelihood estimate. The methods were validated using experimental data acquired at the Sandia National Laboratory Fog Chamber facility (SNLFC). The evolution of the fog particle density and size distribution were measured and used to determine macroscopic absorption and scattering properties using Mie theory. A three-band (0.532, 1.55, and 9.68 𝜇𝜇m) transmissometer with lock-in amplifiers enabled changes in fog density of over an order of magnitude to be measured due to the increased transmission at higher wavelengths, covering both the moderate and highly scattering regimes.

 

This work is supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories, and includes an Academic Alliance with Professor Kevin J. Webb in the Electrical and Computer Engineering Department at Purdue University. Purdue is one of five universities in Sandia’s Academic Alliance program, a program designed to foster collaboration. To deliver on national security missions, Sandia is seeking to hire top talent from and foster research collaborations with Purdue University. Help from University partners is needed to develop scientific and technological breakthroughs.

 

Bio

Brian Z. Bentz is a senior member of technical staff in the Applied Science and Technology Maturation group at Sandia National Laboratories in Albuquerque, New Mexico. Before joining Sandia, he was a graduate student at Purdue University in West Lafayette, Indiana, where he received the Ph.D. degree in electrical engineering in 2017. Brian received the B.Sc. degree in electrical engineering from Purdue University in 2011. His research interests include imaging through scattering media and low temperature plasma diagnostics.

 

Host

Kevin J. Webb, webb@purdue.edu, (765) 494-3373