Dr. Kevin J. Keefer Profile

Dr. Kevin J. Keefer

at Air Force Institute of Technology

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Author

Publications (7)

Proceedings Article | 4 October 2022 Presentation + Paper

Profiling atmospheric turbulence using a non-cooperative target and a camera bank and validating with sonic anemometers

Benjamin Wilson, Arjent Imeri, Santasri Bose-Pillai, Cruz Garcia, Jack McCrae, Henry Raquet, Kevin Keefer, Steven Fiorino

Proceedings Volume 12239, 1223905 (2022) https://doi.org/10.1117/12.2633717

KEYWORDS: Cameras, Turbulence, Profiling, Imaging systems, Atmospheric turbulence, 3D metrology, Spherical lenses, Sensors, Receivers, Ranging

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Proceedings Article | 4 October 2022 Presentation + Paper

Quantifying surface layer moisture flux from MWIR imagery

Steven Fiorino, Yogendra Raut, Laura Slabaugh, Anthony Erickson, Jaclyn Schmidt, Kevin Keefer, Jack McCrae

Proceedings Volume 12239, 1223904 (2022) https://doi.org/10.1117/12.2633977

KEYWORDS: Temperature metrology, Mid-IR, Cameras, Infrared cameras, Thermography, Infrared imaging, Humidity, Infrared sensors, Heat flux, Infrared radiation

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Proceedings Article | 30 September 2022 Presentation + Paper

Comparison of power spectrum and structure function methods to calculate turbulence parameters from sonic anemometer data

Melissa Beason, Stephen Shock, Santasri Bose-Pillai, Jack McCrae, Kevin Keefer, Benjamin Wilson

Proceedings Volume 12237, 122370G (2022) https://doi.org/10.1117/12.2632732

KEYWORDS: Turbulence, Defense and security, Meteorology

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Proceedings Article | 20 August 2020 Paper

Turbulence profiling with a dual beacon Hartmann turbulence sensor using simulation derived weighting functions

Jack McCrae, Santasri Bose-Pillai, Alexander Boeckenstedt, Ben Wilson, Kevin Keefer, Steven Fiorino

Proceedings Volume 11508, 1150806 (2020) https://doi.org/10.1117/12.2568822

KEYWORDS: Turbulence, Telescopes, Device simulation, Atmospheric propagation, Sensors, Profiling, Computer simulations, Optical simulations, Atmospheric turbulence

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Atmospheric turbulence profiles were estimated for a horizontal path based upon measurements made with a dual beacon Hartmann Turbulence Sensor (HTS) using simulation derived weighting functions. These results are compared to estimates made using a weighting functions computed from theory. These results are further compared to anemometer and scintillometer based turbulence estimates for the same path. The previously published theoretical weighting functions for this situation are based upon some presumptions of geometric optics and thus ignore both diffraction and scintillation effects. All of these weighting functions quantify how turbulence at different distances along the path contributes to the expected value of the differential tilt variances measured by the HTS. In the experiment, the HTS used a 16” Meade telescope with 700 subapertures along a 511 m path roughly 2 meters above the ground. Two HeNe lasers separated by 11 cm served as beacons, each was beam expanded to well overfill the telescope aperture. The same situation was simulated with wave optics. To create simulated weighting functions, a single (usually weak) random turbulence screen was inserted at a single plane perpendicularly to the propagation path. Light from one beacon was then numerically propagated to the telescope aperture where the tilts were computed over each subaperture and saved. This propagation was then carried out for the second beacon. This random phase screen was then inserted at a different propagation plane and this procedure was repeated. When all the desired positions along the beam path had been sampled a new random phase screen was generated and this whole procedure was repeated hundreds of times. The desired weighting functions were then generated by computing the differential tilt variance between the beacons and all pairs of horizontally separated subapertures for each path position. All equivalent subaperture separations within each range bin were then averaged together to produce weighting functions which depend on path position and subaperture separation distance. The weighting functions produced in this fashion showed some differences from the theoretical ones. They were a little weaker far from the telescope, and they showed a somewhat broadened notch where the beacons overlapped compared to the theoretical ones. The effect of these differences on the resulting turbulence profile estimates will be discussed.

SPIE Journal Paper | 17 July 2020 Open Access

Implications of four-dimensional weather cubes for improved cloud-free line-of-sight assessments of free-space optical communications link performance

Steven Fiorino, Santasri Bose-Pillai, Jaclyn Schmidt, Brannon Elmore, Kevin Keefer

OE, Vol. 59, Issue 08, 081808, (July 2020) https://doi.org/10.1117/12.10.1117/1.OE.59.8.081808

KEYWORDS: Free space optics, Clouds, Atmospheric optics, Receivers, Optical engineering, Data modeling, Atmospheric modeling, Climatology, Atmospheric propagation

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Proceedings Article | 13 May 2019 Paper

Implications of 4D weather cubes for improved cloud free line of sight assessments of free space optical communications link performance

Steven Fiorino, Santasri Bose-Pillai, Jaclyn Schmidt, Brannon Elmore, Kevin Keefer

Proceedings Volume 10981, 109810S (2019) https://doi.org/10.1117/12.2522344

KEYWORDS: Clouds, Free space optics, Atmospheric propagation, Atmospheric modeling, Receivers, Atmospheric optics, Climatology, Atmospheric particles, Meteorology, Free space optical communications

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This study advances the benefits of previously reported 4D Weather Cubes towards creation of high fidelity cloud free line of sight (CFLOS) beam propagation for realistic assessment of auto-tracked/dynamically routed free space optical communication datalink concepts. 4D Weather Cubes are the product of efficient processing of large, computationally intensive, National Oceanic and Atmospheric Administration (NOAA) gridded numerical weather prediction (NWP) data coupled with embedded physical relationships governing cloud, fog, and precipitation formation to render highly realistic 4D cloud free line of sight analytical environments. The Weather Cubes accrue parameterization of optical effects and custom atmospheric resolution through implementation of the verified and validated Laser Environmental Effects Definition and Reference (LEEDR) atmospheric characterization and radiative transfer code. 4D Weather Cube analyses have recently been expanded to accurately assess Directed Energy weapons and sensor performance (probabilistic climatologies and performance forecasts) at any wavelength/frequency or spectral band in the absence of field test and employment data. The 4D Weather Cubes initialize the High Energy Laser End to End Operational Simulation (HELEEOS) propagation code, which provides a means to dynamically point the communication link. HELEEOS’ calculation of irradiance at the detector as a function of transmission, optical turbulence, and noise sources such as path radiance was the basis for comparative percentile performance binning of FSO communication bit error rates as a function of wide-ranging azimuth/elevation, earth-to-space uplinks. The aggregated, comparative bit error rate binning analyses for different regions, times of day, and seasons using a full year of data provided numerous occasions of clouds, fogs, and precipitation events, thus demonstrating the relevance of 4D Weather Cubes for adroit management of CFLOS opportunities to enhance performance analyses of point-to-point as well as evolving multilayer wireless network concepts.

Proceedings Article | 13 May 2016 Paper

Capturing atmospheric effects on 3D millimeter wave radar propagation patterns

Richard Cook, Steven Fiorino, Kevin Keefer, Jeremy Stringer

Proceedings Volume 9833, 98330E (2016) https://doi.org/10.1117/12.2224007

KEYWORDS: Atmospheric propagation, Radar, 3D modeling, Atmospheric modeling, Extremely high frequency, Wave propagation, Signal attenuation, Scattering, Radiative transfer, Mie scattering

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