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THE SCINTILLATION INDEX IN MODERATE TO STRONG TURBULENCE FOR THE GAUSSIAN BEAM WAVE ALONG A SLANT PATH
Title
THE SCINTILLATION INDEX IN MODERATE TO STRONG TURBULENCE FOR THE GAUSSIAN BEAM WAVE ALONG A SLANT PATH.
Creator
Thomas, Fredrick, Young, Cynthia, University of Central Florida
Abstract / Description
Scintillation is one of the most common statistics in the literature of mathematical modeling of laser propagation through random media. One approach to estimating scintillation is through the Rytov approximation, which is limited to weak atmospheric turbulence. Recently, an improvement of the Rytov approximation was developed employing a linear filter function technique. This modifies the Rytov approximation and extends the validity into the moderate to strong regime. In this work, an...
Show moreScintillation is one of the most common statistics in the literature of mathematical modeling of laser propagation through random media. One approach to estimating scintillation is through the Rytov approximation, which is limited to weak atmospheric turbulence. Recently, an improvement of the Rytov approximation was developed employing a linear filter function technique. This modifies the Rytov approximation and extends the validity into the moderate to strong regime. In this work, an expression governing scintillation of a Gaussian beam along an uplink slant path valid in all regimes of turbulence is presented, as well as results for the limiting cases of a plane wave and a spherical wave.
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Date Issued
2005
Identifier
CFE0000670, ucf:46509
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000670
EFFECTS OF ATMOSPHERIC TURBULENCE ON THE PROPAGATION OF FLATTENED GAUSSIAN OPTICAL BEAMS
Title
EFFECTS OF ATMOSPHERIC TURBULENCE ON THE PROPAGATION OF FLATTENED GAUSSIAN OPTICAL BEAMS.
Creator
Cowan, Doris, Andrews, Larry, University of Central Florida
Abstract / Description
In an attempt to mitigate the effects of the atmosphere on the coherence of an optical (laser) beam, interest has recently been shown in changing the beam shape to determine if a different power distribution at the transmitter will reduce the effects of the random fluctuations in the refractive index. Here, a model is developed for the field of a flattened Gaussian beam as it propagates through atmospheric turbulence, and the resulting effects upon the scintillation of the beam and upon beam...
Show moreIn an attempt to mitigate the effects of the atmosphere on the coherence of an optical (laser) beam, interest has recently been shown in changing the beam shape to determine if a different power distribution at the transmitter will reduce the effects of the random fluctuations in the refractive index. Here, a model is developed for the field of a flattened Gaussian beam as it propagates through atmospheric turbulence, and the resulting effects upon the scintillation of the beam and upon beam wander are determined. A comparison of these results is made with the like effects on a standard TEM00 Gaussian beam. The theoretical results are verified by comparison with a computer simulation model for the flattened Gaussian beam. Further, a determination of the probability of fade and of mean fade time under weak fluctuation conditions is determined using the widely accepted lognormal model. Although this model has been shown to be somewhat optimistic when compared to results obtained in field tests, it has value here in allowing us to compare the effects of atmospheric conditions on the fade statistics of the FGB with those of the lowest order Gaussian beam. The effective spot size of the beam, as it compares to the spot size of the lowest order Gaussian beam, is also analyzed using Carter's definition of spot size for higher order Gaussian beams.
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Date Issued
2006
Identifier
CFE0001377, ucf:46969
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001377
ON THE USE OF GAUSSIAN FILTER FUNCTIONS FOR ADAPTIVE OPTICS
Title
ON THE USE OF GAUSSIAN FILTER FUNCTIONS FOR ADAPTIVE OPTICS.
Creator
Assad, Merfit, Andrews, Larry, University of Central Florida
Abstract / Description
For adaptive optic systems, the use of aperture filter functions calculated using various Zernike modes can be useful in removing lower-order aberrations caused by atmospheric turbulence. Traditionally, these filter functions are calculated using the step function depicting a hard aperture that introduces integrals that are sometimes difficult to integrate and must be done numerically. The Gaussian method can be used in place of the conventional method for calculating the aperture filter...
Show moreFor adaptive optic systems, the use of aperture filter functions calculated using various Zernike modes can be useful in removing lower-order aberrations caused by atmospheric turbulence. Traditionally, these filter functions are calculated using the step function depicting a hard aperture that introduces integrals that are sometimes difficult to integrate and must be done numerically. The Gaussian method can be used in place of the conventional method for calculating the aperture filter functions. Evaluation of the Gaussian approximation for modeling a finite receiver aperture can be made by comparison of reduction in phase variance with results achieved using the conventional method. The validity of Gaussian approximation in this application is demonstrated by the consistency of results between the two methodologies. Comparison of reduction in scintillation by the two methodologies reveals several benefits derived from utilization of Gaussian approximation. The Gaussian approximation produces data that can be interpreted analytically. It further produces greater scintillation reduction. This paper will first examine the use of statistical models for predicting atmospheric turbulence and then the use of Zernike polynomials in adaptive optics. Next, this paper compares the reduction of phase variance and scintillation using the conventional method with the Gaussian approximation to evaluate the effectiveness of the new filter functions. The results of these comparisons are presented both as mathematical expressions and graphically.
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Date Issued
2006
Identifier
CFE0001436, ucf:52885
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001436
Effect of Nonclassical Optical Turbulence on a Propagating Laser Beam
Title
Effect of Nonclassical Optical Turbulence on a Propagating Laser Beam.
Creator
Beason, Melissa, Phillips, Ronald, Atia, George, Richardson, Martin, Andrews, Larry, Shivamoggi, Bhimsen, University of Central Florida
Abstract / Description
Theory developed for the propagation of a laser beam through optical turbulence generally assumes that the turbulence is both homogeneous and isotropic and that the associated spectrum follows the classical Kolmogorov spectral power law of . If the atmosphere deviates from these assumptions, beam statistics such as mean intensity, correlation, and scintillation index could vary significantly from mathematical predictions. This work considers the effect of nonclassical turbulence on a...
Show moreTheory developed for the propagation of a laser beam through optical turbulence generally assumes that the turbulence is both homogeneous and isotropic and that the associated spectrum follows the classical Kolmogorov spectral power law of . If the atmosphere deviates from these assumptions, beam statistics such as mean intensity, correlation, and scintillation index could vary significantly from mathematical predictions. This work considers the effect of nonclassical turbulence on a propagated beam. Namely, anisotropy of the turbulence and a power law that deviates from . A mathematical model is developed for the scintillation index of a Gaussian beam propagated through nonclassical turbulence and theory is extended for the covariance function of intensity of a plane wave propagated through nonclassical turbulence. Multiple experiments over a concrete runway and a grass range verify the presence of turbulence which varies between isotropy and anisotropy. Data is taken throughout the day and the evolution of optical turbulence is considered. Also, irradiance fluctuation data taken in May 2018 over a concrete runway and July 2018 over a grass range indicate an additional beam shaping effect. A simplistic mathematical model was formulated which reproduced the measured behavior of contours of equal mean intensity and scintillation index.?
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Date Issued
2018
Identifier
CFE0007310, ucf:52646
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007310
FADE STATISTICS FOR A LASERCOM SYSTEM AND THE JOINT PDF OF A GAMMA-GAMMA DISTRIBUTED IRRADIANCE AND ITS TIME DERIVATIVE
Title
FADE STATISTICS FOR A LASERCOM SYSTEM AND THE JOINT PDF OF A GAMMA-GAMMA DISTRIBUTED IRRADIANCE AND ITS TIME DERIVATIVE.
Creator
Stromqvist Vetelino, Frida, Young, Cynthia, University of Central Florida
Abstract / Description
The performance of lasercom systems operating in the atmosphere is reduced by optical turbulence, which causes irradiance fluctuations in the received signal. The result is a randomly fading signal. Fade statistics for lasercom systems are determined from the probability density function (PDF) of the irradiance fluctuations. The expected number of fades per second and their mean fade time require the joint PDF of the fluctuating irradiance and its time derivative. Theoretical integral...
Show moreThe performance of lasercom systems operating in the atmosphere is reduced by optical turbulence, which causes irradiance fluctuations in the received signal. The result is a randomly fading signal. Fade statistics for lasercom systems are determined from the probability density function (PDF) of the irradiance fluctuations. The expected number of fades per second and their mean fade time require the joint PDF of the fluctuating irradiance and its time derivative. Theoretical integral expressions, as well as closed form, analytical approximations, were developed for the joint PDF of a gamma-gamma distributed irradiance and its time derivative, and the corresponding expression for the expected number of fades per second. The new approximation for the conditional PDF of the time derivative of a gamma-gamma irradiance is a zero mean Gaussian distribution, with a complicated irradiance depending variance. Fade statistics obtained from experimental data were compared to theoretical predictions based on the lognormal and gamma-gamma distributions. A Gaussian beam wave was propagated through the atmosphere along a horizontal path, near ground, in the moderate-to-strong optical turbulence. To characterize the propagation path, a new method that infers atmospheric propagation parameters was developed. Scintillation theory combined with a numerical scheme was used to infer the structure constant, Cn2, the inner scale and the outer scale from the optical measurements. The inferred parameters were used in calculations for the theoretical PDFs. It was found that fade predictions made by the gamma-gamma and lognormal distributions provide an upper and lower bound, respectively, for the probability of fade and the number of fades per second for irradiance data collected in the moderate-to-strong fluctuation regime. Aperture averaging effects on the PDF of the irradiance fluctuations were investigated by comparing the irradiance distributions for the three receiver apertures at two different values of the structure parameter and, hence, different values of the coherence radius. For the moderate-to-strong fluctuation regime, the gamma-gamma distribution provides a good fit to the irradiance fluctuations collected by finite-sized apertures that are significantly smaller than the coherence radius. For apertures larger than or equal to the coherence radius, the irradiance fluctuations appear to be lognormally distributed.
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Date Issued
2006
Identifier
CFE0001440, ucf:47069
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001440
MEASURING OPTICAL TURBULENCE PARAMETERS WITH A THREE-APERTURE RECEIVER
Title
MEASURING OPTICAL TURBULENCE PARAMETERS WITH A THREE-APERTURE RECEIVER.
Creator
Wayne, David, Phillips, Ronald, University of Central Florida
Abstract / Description
This thesis discusses methods to measure several atmospheric parameters related to turbulence. Techniques used by two different scintillometers based on weak turbulence theory are discussed along with a method to estimate the inner scale developed by Hill. The theory and minimization algorithm used to infer the atmospheric parameters are discussed. The main focus is on the analysis and collection of experimental data with a three-aperture receiver system. Intensity fluctuations from a CW...
Show moreThis thesis discusses methods to measure several atmospheric parameters related to turbulence. Techniques used by two different scintillometers based on weak turbulence theory are discussed along with a method to estimate the inner scale developed by Hill. The theory and minimization algorithm used to infer the atmospheric parameters are discussed. The main focus is on the analysis and collection of experimental data with a three-aperture receiver system. Intensity fluctuations from a CW laser source are collected over a 1km path with three different receiving apertures. The scintillation index is found for each receiving aperture and recently developed theory for all regimes of optical turbulence is used to infer three atmospheric parameters, Cn2, l0, and L0. The transverse wind speed is also calculated from the experimental data using a cross-correlation technique. Parallel to the three-aperture data collection is a commercial scintillometer unit which reports Cn2 and crosswind speed. There is also a weather station positioned at the receiver side which provides point measurements for temperature and wind speed. The Cn2 measurement obtained from the commercial scintillometer is used to infer l0, L0, and the scintillation index. Those values are then compared to the inferred atmospheric parameters from the experimental data. Hill's method is used as an estimate to l0 based upon path-averaged wind speed and is compared to the inferred l0 values. The optimal aperture sizes required for three-aperture data collection are presented. In closing, the technique for measuring crosswind speed is discussed along with the ideal aperture size and separation distance for data collection. Suggestions are offered for future experimentation.
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Date Issued
2006
Identifier
CFE0001393, ucf:46974
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001393
Robust Subspace Estimation Using Low-Rank Optimization. Theory and Applications in Scene Reconstruction, Video Denoising, and Activity Recognition.
Title
Robust Subspace Estimation Using Low-Rank Optimization. Theory and Applications in Scene Reconstruction, Video Denoising, and Activity Recognition.
Creator
Oreifej, Omar, Shah, Mubarak, Da Vitoria Lobo, Niels, Stanley, Kenneth, Lin, Mingjie, Li, Xin, University of Central Florida
Abstract / Description
In this dissertation, we discuss the problem of robust linear subspace estimation using low-rank optimization and propose three formulations of it. We demonstrate how these formulations can be used to solve fundamental computer vision problems, and provide superior performance in terms of accuracy and running time.Consider a set of observations extracted from images (such as pixel gray values, local features, trajectories...etc). If the assumption that these observations are drawn from a...
Show moreIn this dissertation, we discuss the problem of robust linear subspace estimation using low-rank optimization and propose three formulations of it. We demonstrate how these formulations can be used to solve fundamental computer vision problems, and provide superior performance in terms of accuracy and running time.Consider a set of observations extracted from images (such as pixel gray values, local features, trajectories...etc). If the assumption that these observations are drawn from a liner subspace (or can be linearly approximated) is valid, then the goal is to represent each observation as a linear combination of a compact basis, while maintaining a minimal reconstruction error. One of the earliest, yet most popular, approaches to achieve that is Principal Component Analysis (PCA). However, PCA can only handle Gaussian noise, and thus suffers when the observations are contaminated with gross and sparse outliers. To this end, in this dissertation, we focus on estimating the subspace robustly using low-rank optimization, where the sparse outliers are detected and separated through the `1 norm. The robust estimation has a two-fold advantage: First, the obtained basis better represents the actual subspace because it does not include contributions from the outliers. Second, the detected outliers are often of a specific interest in many applications, as we will show throughout this thesis. We demonstrate four different formulations and applications for low-rank optimization. First, we consider the problem of reconstructing an underwater sequence by removing the turbulence caused by the water waves. The main drawback of most previous attempts to tackle this problem is that they heavily depend on modelling the waves, which in fact is ill-posed since the actual behavior of the waves along with the imaging process are complicated and include several noise components; therefore, their results are not satisfactory. In contrast, we propose a novel approach which outperforms the state-of-the-art. The intuition behind our method is that in a sequence where the water is static, the frames would be linearly correlated. Therefore, in the presence of water waves, we may consider the frames as noisy observations drawn from a the subspace of linearly correlated frames. However, the noise introduced by the water waves is not sparse, and thus cannot directly be detected using low-rank optimization. Therefore, we propose a data-driven two-stage approach, where the first stage (")sparsifies(") the noise, and the second stage detects it. The first stage leverages the temporal mean of the sequence to overcome the structured turbulence of the waves through an iterative registration algorithm. The result of the first stage is a high quality mean and a better structured sequence; however, the sequence still contains unstructured sparse noise. Thus, we employ a second stage at which we extract the sparse errors from the sequence through rank minimization. Our method converges faster, and drastically outperforms state of the art on all testing sequences. Secondly, we consider a closely related situation where an independently moving object is also present in the turbulent video. More precisely, we consider video sequences acquired in a desert battlefields, where atmospheric turbulence is typically present, in addition to independently moving targets. Typical approaches for turbulence mitigation follow averaging or de-warping techniques. Although these methods can reduce the turbulence, they distort the independently moving objects which can often be of great interest. Therefore, we address the problem of simultaneous turbulence mitigation and moving object detection. We propose a novel three-term low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the background, the turbulence, and the object. We simplify this extremely difficult problem into a minimization of nuclear norm, Frobenius norm, and L1 norm. Our method is based on two observations: First, the turbulence causes dense and Gaussian noise, and therefore can be captured by Frobenius norm, while the moving objects are sparse and thus can be captured by L1 norm. Second, since the object's motion is linear and intrinsically different than the Gaussian-like turbulence, a Gaussian-based turbulence model can be employed to enforce an additional constraint on the search space of the minimization. We demonstrate the robustness of our approach on challenging sequences which are significantly distorted with atmospheric turbulence and include extremely tiny moving objects. In addition to robustly detecting the subspace of the frames of a sequence, we consider using trajectories as observations in the low-rank optimization framework. In particular, in videos acquired by moving cameras, we track all the pixels in the video and use that to estimate the camera motion subspace. This is particularly useful in activity recognition, which typically requires standard preprocessing steps such as motion compensation, moving object detection, and object tracking. The errors from the motion compensation step propagate to the object detection stage, resulting in miss-detections, which further complicates the tracking stage, resulting in cluttered and incorrect tracks. In contrast, we propose a novel approach which does not follow the standard steps, and accordingly avoids the aforementioned difficulties. Our approach is based on Lagrangian particle trajectories which are a set of dense trajectories obtained by advecting optical flow over time, thus capturing the ensemble motions of a scene. This is done in frames of unaligned video, and no object detection is required. In order to handle the moving camera, we decompose the trajectories into their camera-induced and object-induced components. Having obtained the relevant object motion trajectories, we compute a compact set of chaotic invariant features, which captures the characteristics of the trajectories. Consequently, a SVM is employed to learn and recognize the human actions using the computed motion features. We performed intensive experiments on multiple benchmark datasets, and obtained promising results.Finally, we consider a more challenging problem referred to as complex event recognition, where the activities of interest are complex and unconstrained. This problem typically pose significant challenges because it involves videos of highly variable content, noise, length, frame size ... etc. In this extremely challenging task, high-level features have recently shown a promising direction as in [53, 129], where core low-level events referred to as concepts are annotated and modeled using a portion of the training data, then each event is described using its content of these concepts. However, because of the complex nature of the videos, both the concept models and the corresponding high-level features are significantly noisy. In order to address this problem, we propose a novel low-rank formulation, which combines the precisely annotated videos used to train the concepts, with the rich high-level features. Our approach finds a new representation for each event, which is not only low-rank, but also constrained to adhere to the concept annotation, thus suppressing the noise, and maintaining a consistent occurrence of the concepts in each event. Extensive experiments on large scale real world dataset TRECVID Multimedia Event Detection 2011 and 2012 demonstrate that our approach consistently improves the discriminativity of the high-level features by a significant margin.
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Date Issued
2013
Identifier
CFE0004732, ucf:49835
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004732