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A laser-illuminating imaging system operating in the presence of atmospheric turbulence will encounter several sources of noise and diffraction induced errors that can make postprocessing the images difficult. As the beam propagates, turbulence induced tilt will cause the beam to wander off axis from the target. This is especially troublesome in imaging satellites due to most turbulence being closer to the Earth's surface and greatly affecting the beam in the early stages of propagation. Additionally, the returning beam convoluted with the target will encounter turbulence induced tilt that appears as apparent movement of the target between image frames. This results in varying beam intensities at the target surface between imaging frames that can affect registration algorithms. Using simulated data, an algorithm using expectation maximization and least squares techniques was developed that has the ability to estimate both the tilt of the pulsed laser beam and the apparent movement of the object between incoherent frames separately and produce a superior estimate of the target. The results from this algorithm can be used to reduce the effects of beam wander and thus increase the SNR of postprocessed images., In Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), 11-14 Sep 2012, Maui, HI.

This paper develops a new, more accurate temporal model of phase screen generation. The long standing Fourier transform (FT) based method assumes the frozen flow hypothesis holds, where large phase screens are generated and then shifted. The result is a statistically correct screen. Realistically however, the phase changes with time especially when the wind velocity is small or non-existent. The temporal evolution method proposed in this paper is based on expanding a random walk algorithm to the Zernike polynomial method that will include not only the perceived shifting of the Kolmogorov FT method, but also a boiling effect that changes the phase as it shifts. This new method of phase screen generation will be validated through a simulated experiment which measures the correlations of tilt as a function of time and compares that to a predicted tilt correlation function derived using both the frozen flow hypothesis and the new model. This validation experiment will show that the frozen flow model alone fails to accurately predict the temporal correlation of optical tilt., Presented at the 13th annual Advanced Maui Optical and Space Surveillance (AMOS) Technologies Conference on September 11-14, 2012 Maui, Hawaii. The original document contains color images.

One of the realities in astronomical imaging from the earth is the negative affects introduced by the atmosphere. In order to counteract the effects of the atmosphere, we propose the use of a 3 channel polarimeter and associated deconvolution algorithm. The 3 channel polarimeter features three focal plane arrays simultaneously imaging the same object through polarization analyzers set at angles of 45 and 90 degrees relative to the reference channel. The algorithm is designed to estimate the intensity of the object, its degree of polarization and a term related to its angle of polarization when performing long exposure imaging and the seeing parameter of the atmosphere is known. The results show that a degraded image of 2 point sources can be successfully reconstructed into an image resembling the actual image before the degradation occurred., 2010 Advanced Maui Optical and Space Surveillance Technologies Conference, 14-17 Sep, Maui, HI. The original document contains color images.

Three dimensional (3D) FLASH Laser Radar (LADAR) sensors are unique due to the ability to rapidly acquire a series of two dimensional remote scene data (i.e. range images). Principal causes of 3D FLASH LADAR range estimation error include spatial blur, detector blurring, noise, timing jitter, and inter-sample targets. Unlike previous research, this paper accounts for pixel coupling by defining the range image mathematical model as a 2D convolution between the system spatial impulse response and the object (target or remote scene) at a particular point in time. Using this model, improved range estimation is possible by object restoration from the data observations. Object estimation is performed by deriving a blind deconvolution Generalized Expectation Maximization (GEM) algorithm with the range determined from the estimated object by a normalized correlation method. Theoretical derivations and simulation results are verified with experimental data of a bar target taken from a 3D FLASH LADAR system in a laboratory environment. Simulation examples show that the GEM improves range estimation over the unprocessed data and a Wiener filter method by 75% and 26% respectively. In the laboratory experiment, the GEM improves range estimation by 34% and 18% over the unprocessed data and Wiener filter method respectively., Published in the Journal of Applied Remote Sensing, v4 n043517 p1-28, 19 March 2010.

The purpose of the algorithm developed in this thesis was to create a post processing method that could resolve objects at low signal levels using polarization diversity and no knowledge of the atmospheric seeing conditions. The process uses a two-channel system, one unpolarized image and one linearly polarized image, in a GEM algorithm to reconstruct the object. Previous work done by Strong showed that a two-channel system using polarization diversity on short exposure imagery could produce images up to twice the diffraction limit. In this research, long exposure images were simulated and a simple Kolmogorov model used. This allowed for the atmosphere to be characterized by single parameter, the Fried Parameter. Introducing a novel polarization prior that restricts the polarization parameter, it was possible to determine the Fried Parameter to within half a centimeter without any addition knowledge or processes. It was also found that when high polarization diversity was present in the image could be reconstructed with significantly better resolution and signal level did not affect this resolving capability. At very low signal levels, imagery with low to no diversity could not be resolved at all whereas high diversity resolved equally as well as if there was a high signal level. Current algorithms being used do not include polarization diversity but can substantially improve resolution. Application of this algorithm could be used in dim-object detection around satellites as well as solar surface imagery., Advanced Maui Optical and Space Surveillance Technologies Conference held 1-4 September 2009 in Maui, HI. The original document contains color images.