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Signal Processing Commons

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Air Force Institute of Technology

Atmospheric turbulence

Atmospheric Sciences

Publication Year

Articles 1 - 3 of 3

Full-Text Articles in Signal Processing

Determining The Index Of Refraction Of An Unknown Object Using Passive Polarimetric Imagery Degraded By Atmospheric Turbulence, Milo W. Hyde Iv Sep 2010

Determining The Index Of Refraction Of An Unknown Object Using Passive Polarimetric Imagery Degraded By Atmospheric Turbulence, Milo W. Hyde Iv

Theses and Dissertations

In this research, an algorithm is developed to estimate the index of refraction of an unknown object using passive polarimetric images degraded by atmospheric turbulence. The algorithm uses a variant of the maximum-likelihood blind-deconvolution algorithm developed by LeMaster and Cain to recover the true object (i.e., the first Stokes parameter), the degree of linear polarization, and the polarimetric-image point spread functions. Nonlinear least squares is then used to find the value of the complex index of refraction which best fits the theoretical degree of linear polarization, derived using a polarimetric bidirectional reflectance distribution function, to the turbulence-corrected degree of linear …


Performance Analysis Of A Hartman Wavefront Sensor Used For Sensing Atmospheric Turbulence Statistics, Toby D. Reeves Dec 1996

Performance Analysis Of A Hartman Wavefront Sensor Used For Sensing Atmospheric Turbulence Statistics, Toby D. Reeves

Theses and Dissertations

Atmospheric turbulence parameters, such as Fried's coherence diameter, the outer scale of turbulence, and the turbulence power law, are related to the wavefront slope structure function (SSF). The SSF is defined as the second moment of the wavefront slope difference as a function of both time and position. Knowledge of the SSF allows turbulence parameters to be estimated. Hartmann wavefront sensor (H-WFS) slope measurements composed of both signal and noise, allow the SSF to be estimated by computing a mean square difference of H-WFS slope measurements. The quality of the SSF estimate is quantified by the signal-to-noise ratio (SNR) of …


The Role Of Frame Selection And Bispectrum Phase Reconstruction For Speckle Imaging Through Atmospheric Turbulence, Elizabeth A. Harpold Dec 1995

The Role Of Frame Selection And Bispectrum Phase Reconstruction For Speckle Imaging Through Atmospheric Turbulence, Elizabeth A. Harpold

Theses and Dissertations

Frame selection using quality sharpness metrics have been shown in previous AFIT theses, to be effective in improving the final product of images obtained using adaptive optics. This thesis extends this idea to noncompensated speckle image data. Speckle image reconstruction is simulated with and without frame selection. Speckle images require the processing of hundreds of data frames. Frame selection is a method of reducing the amount of data required to reconstruct the image. A collection of short exposure image data frames of a single object are sorted based on sharpness metrics. Only the highest quality frames are retained and processed …