This PDF file contains the front matter associated with SPIE Proceedings Volume 12239, including the Title Page, Copywrite Information, Table of Contents, and Conference Committee Page.

Tatarskii’s first book on wave propagation through the turbulent atmosphere was published in English in 1961 and describes what we refer to as the classical theory of optical turbulence. It relies on a number of simplifying assumptions, such as the assumption of locally homogeneous and isotropic, fully developed turbulence; the Corrsin-Obukhov similarity theory; Taylor’s frozen-turbulence hypothesis; and the assumption of weak scattering. In this invited presentation, we review and discuss non-classical models of optical turbulence, which account for non-classical effects and phenomena, including anisotropy, intermittency, outer-scale effects, and non-Gaussianity of refractive-index increments.

An implementation of differential scintillations to characterize the C²_n(z) profile along a nearly horizontal propagation path measured by a Shack-Hartmann wavefront sensor is developed and demonstrated. Measurements from a Small Mobile Atmospheric Sensing Hartmann (SMASH) instrument using an LED source to characterize 500 m, 1 km and 2 km paths at the Environmental Laser Test Facility (ELTF) are presented.

Psychrometric measurements via sling psychrometers have long been the standard for quantifying thermodynamics of near-surface atmospheric gas-vapor mixtures, specifically moisture parameters. However, these devices are generally only used to measure temperature and humidity at one near-surface level. Multiple self-aspirating psychrometers can be used in a vertical configuration to measure temperature and moisture gradients and fluxes in the first 1-2 meters of the surface layer. This study evaluates the feasibility of a method using infrared (IR) imagery, and a mini-tower of wet and dry paper towels to psychometrically obtain surface layer temperature and moisture gradients and fluxes. First, the possible utility of using a single IR thermometer/detector to evaluate moisture and heat fluxes near the surface was explored, and it was found that the single IR sensor could be used to sense wet- and dry-bulb temperature changes of 0.7 K and 0.6 K respectively over vertical distances as small as 50 cm, thus allowing surface layer temperature and moisture gradients/fluxes to be quantified. The feasibility of this single IR detector method to provide with reasonable certainty values of surface layer heat and moisture fluxes suggests the technique could be exploited with more efficiency and accuracy with a calibrated imaging IR camera or sensor array. The surface layer dry- and wet-bulb temperatures obtained using an MWIR camera system are compared to Kestrel 4000 Weather Meter and Bacharach sling psychrometer measurements under various atmospheric conditions and surface types to test the viability of the method. Uncertainty statistics are calculated and evaluated to quantify effectiveness.

Knowledge of turbulence distribution along a path can be useful for effective compensation in a highly anisoplanatic situation. In an earlier work, a method to profile turbulence using time-lapse imagery of a distant building from two spatially separated cameras was demonstrated. By using multiple cameras instead of just a pair, the profiling resolution as well as the fraction of the path that can be reasonably profiled can be improved. This idea is demonstrated by using 5 spatially separated cameras capturing images of a distant target with features on it. Extended features on the target are tracked and by measuring the variances of the difference in wavefront tilts sensed between cameras due to all pairs of target features, turbulence information along the imaging path can be extracted. The mathematical framework is discussed and the profiling results are compared against point measurements from a 3D sonic anemometer placed onboard an unmanned aerial system which is driven along the imaging path. The method is relatively low cost and does not require sophisticated instrumentation. Turbulence can be sensed remotely from a single site without deployment of sources or sensors at the target location. Additionally, the method is phase-based, and hence has an advantage over irradiance-based techniques which suffer from saturation issues at long ranges. By imaging elevated targets in the future, turbulence changes with altitude can be investigated as well.

The inner scale plays a critical role in beam scintillation and branch point evolution in optical propagation through atmospheric turbulence. Understanding this parameter, in-situ, during experiments is therefore of great interest. We compare different methods of estimating the inner scale using AFRL’s Small Mobile Atmospheric Sensing Hartmann (SMASH). The investigations are conducted with data collected at Kirtland, AFB in New Mexico along multiple paths varying from weak to strong irradiance fluctuation conditions.

Sonic detection and ranging (SODAR) is a technique for measuring wind speed and turbulence parameters from backscattered sound waves. The SODAR projects a beam of sound straight up, as well as at angles slightly off vertical. Sound waves are scattered by variations in the density of the air and are then received back at the SODAR, the time of flight giving the height being probed. Doppler shifts provide information about the wind velocity. Since larger variations in the local density of the atmosphere imply higher turbulence, backscatter strength is related to turbulence. The instrument used here was a Scintec MFAS flat array SODAR. While the backscatter strength thus appears to be a direct indicator of the turbulence strength, calibration and an estimate of the variation of temperature with height is needed to process this strength into values for CT2 and Cn2. Consequently, it is interesting to compare measurements from this technique with results from other turbulence measurement approaches. A sonic anemometer measures the wind velocity and temperature over the volume of air between its probes. From this instrument, turbulence is estimated by the temperature variations in the air moved past the instrument by the wind. The sonic anemometer measures turbulence at a single location, while the SODAR measures turbulence as a function of height (up to about 400 meters above ground). Thus these comparisons aren’t really looking at the same thing. By mounting the sonic anemometer on a small UAV, this difficulty can be overcome.

We discuss the capability of adaptive optics to increase the performance of laser systems operating in atmospheric turbulence. Our approach is based on the Zernike filter functions, Taylor’s frozen flow hypothesis and bandwidth limitations of a realistic servo control system. System performance is analyzed in terms of the Strehl ratio on target. Our results indicate that adaptive optics can be effective even when engaging fast moving targets and that moderate closed-loop bandwidths of ~100 Hz would suffice for most analyzed scenarios. Applications of interest are beam delivery systems and free space optical communications.

Earth observation satellite with a large mirror telescope has been studied in Japan Aerospace Exploration Agency to offer a fine ground sampling distance from geostationary orbit. Our latest optical design has a 3.6-m primary mirror and thus aims to obtain a ground sampling distance of sub-10 m from an altitude of 36,000 km at visible wavelengths. To achieve diffraction-limited performance in such optics on orbit, the telescope equips with not only actuators to control primary and secondary mirrors but also a deformable mirror (DM) at the exit pupil plane for the fine phasing. For on-orbit wavefront correction with a deformable mirror, a wavefront sensorless image-based aberration correction scheme is advantageous from the viewpoint of severely limited hardware resources in satellites. Phase diversity (PD) and stochastic parallel gradient descent (SPGD) optimization are known for promising image-based approaches. The former is model-based and thus the estimation accuracy of wavefront aberration significantly depends on the model accuracy, while the other requires many measurements to compensate for large aberration. To alleviate these issues, we propose sequential use of PD and SPGD optimization to efficiently reduce wavefront correction. We first developed an optical testbed with an incoherent light source, a MEMS DM, and extended image targets, and then a wavefront correction experiment was carried out. As a result, the proposed method successfully achieved diffraction-limited imaging performance with a small number of measurements. We will also discuss the image dependence of the wavefront correction performance.

Recently, we proposed a deep-learning (DL) -based method for solving coherent imaging inverse problems, known as coherent plug and play (CPnP). CPnP is a regularized inversion framework that works with coherent imaging data corrupted by phase errors. The algorithm jointly produces a focused and speckle-free image and an estimate of the phase errors. The algorithm combines physics-based propagation models with image models learned with DL and produces higher-quality estimates when compared to other non-DL methods. Previously, we were only able to demonstrate CPnP using simulated data. In this work, we design a coherent imaging test bed to validate CPnP using real data. We devise a method to obtain truth data for both the images and the phase errors. This allows us to quantify performance and compare different algorithms. Our results validate the improved performance of CPnP when compared to other existing methods.

Flow measurements inside oscillating droplets are crucial for revealing potentials to save energy. However, aberrations of the light at the surface of the droplets results. In this paper, a novel measurement system is presented for both 3D imaging with only one camera and aberration correction. 3D imaging is achieved by introducing a Double-Helix Point Spread Function. The dynamically introduced aberrations of the phase boundary are measured with a Fresnel Guide Star and are corrected with a deformable mirror in a closed-loop system with low latency.

Digital holography enables 3D imagery after processing frequency-diverse stacks of 2D coherent images obtained from a chirped-frequency illuminator. To compensate for object motion or vibration, a constant temporal frequency or “pilot tone” illuminator can act as a reference for each chirped frequency. This paper examines speckle decorrelation between the chirped and pilot tone illuminators and its effect on the resultant range images. We show that speckle decorrelation between the two illuminators is more severe for facets of the object’s surface that are more highly sloped, relative to the optical axis, and that this decorrelation results in noise in the range images in the areas of the object that are highly sloped. We examine the severity of this noise as a function of several imaging parameters and show that post-processing apodization, effectively reducing bandwidth, in the temporal frequency domain can reduce this noise.

Imaging through deep turbulence is a hard and unsolved problem. There have been recent advances toward sensing and correcting moderate turbulence using digital holography (DH). With DH, we use optical heterodyne detection to sense the amplitude and phase of the light reflected from an object. This phase information allows us to digitally back propagate the measured field to estimate and correct distributed-volume aberrations. Recently, we developed a model-based iterative reconstruction (MBIR) algorithm for sensing and correcting atmospheric turbulence using multi-shot DH data (i.e., multiple holographic measurements). Using simulation, we showed the ability to correct deep-turbulence effects, loosely characterized by Rytov numbers greater than 0.75 and isoplanatic angles near the diffraction limited viewing angle. In this work, we demonstrate the validity of our method using laboratory measurements. Our experiments utilized a combination of multiple calibrated Kolmogorov phase screens along the propagation path to emulate distributed-volume turbulence. This controlled laboratory setup allowed us to demonstrate our algorithm’s performance in deep turbulence conditions using real data.

This paper gives new insights on Laser Range Profiling. In this paper we explore the advantages of Laser Range Profiling or High Temporal Resolution Ladar (Laser Detection and Ranging) to identify objects at long ranges with high probability. In the first part of the paper, we explain the concept of Laser Range Profiling. In the second part of the paper, we focus on the electromagnetic simulation of laser range profiles of different objects. In the third part of the paper, we study the identification function of the system, and describe an algorithm which correlates the measured signatures of the unknown object with the closest range profile related to the aspect angles of the object in the database.

Wind tunnel experiments were conducted to measure the unsteady surface pressure field of a hemisphere-on-cylinder turret in subsonic flow. These measurements were obtained using pressure transducers coupled with fast response pressure sensitive paint. The surface pressure field data resulting from Mach 0.5 flow (ReD ≈ 2 × 10⁶ ) over three different turret protrusion distances were analyzed. Previously, dominant surface pressure modes on the turret were found using proper orthogonal decomposition. The results of which showed that greater turret protrusion into the freestream flow increased the prevalence of spanwise anti-symmetric surface pressure field fluctuations. These anti-symmetric pressure fluctuations are caused by anti-symmetrical vortex shedding. However, when a partially submerged hemispherical turret geometry is used, it was shown that this anti-symmetric mode was of much lower relative energy. This suggests that there is a transition in flow field phenomena as protrusion is changed from partially submerged to a full hemisphere configuration. Further investigation into this so-called “mode switching” is the emphasis of the work presented here. This research heavily relied on modal analysis to identify correlations between turret and wake surface pressure fields. The fluctuations in the surface pressure field around the partial hemisphere were found to be mostly dominated by the wake with little influence from fluidic structures on the turret itself. For the hemisphere and hemisphere-on-cylinder configurations, both symmetric and anti-symmetric unsteady separation grew to be the largest influence and was coupled with the wake fluctuations.

This paper tells the background leading to the realization that a flight-test program to study aero-optics was needed. The remaining part of the paper describes many of the tasks that had to be learned in order to actually develop the flight program known as the Airborne Aero-Optic Laboratory. In addition to the development of the laboratory aircraft some of the development of the specialized equipment and operational methods are described.

Predictive Adaptive Optics (AO) control is a promising technology for AO applications in high-disturbance and low-signal environments such as directed energy, optical communication, and astronomical seeing. Predictive AO utilizes future state predictions of an optical wavefront propagated through a turbulent medium to drive correction, thereby mitigating the limits imposed by inherent latency in the AO system. In this work, we present a novel Artificial Neural Network (ANN) approach for embedding the flow dynamics for a range of Airborne Aero-Optics Laboratory (AAOL) datasets into a single turbulent flow prediction model. As the angle of the laser beam through the hemispherical AAOL turret changes, flow characteristics vary greatly according to statistics such as mean advection speed, direction, and scale, as well as the presence of different turbulent structures and shock waves. As a result, a predictive model trained on a single look angle and flow condition will likely have poor performance when conditions change, for instance, by slewing the turret look angle during AO operation. In our approach, this limitation is mitigated by introducing the model to flow data from a range of look angles during training. We analyze this combined model’s ability to forecast turbulent wavefronts from look angles included in the training set to establish baseline model performance. We then consider performance on measured AAOL wavefront sensor data from holdout look angles entirely excluded from the training wavefront data to demonstrate the generalization capability of the resulting model, and consider the implications for ANN-based AO correction for dynamic, high-speed, turbulent flows.

This video was presented for this conference "Unconventional Imaging and Adaptive Optics 2022" 12239-18

Shock waves result from turning supersonic or locally supersonic flow and result in a large change in gas properties downstream of the shock. This change in gas properties, namely, the large increase in freestream density can affect the wavefront of a laser beam propagating through the shock. In this paper, analytic expressions are developed to describe the effects of these shock waves on the wavefront a laser beam propagating through the shock both parallel and on an angle relative to the shock direction. Furthermore, these near-field disturbances are then brought to a focus at the image-plane using a thin lens transmittance function with the Fresnel diffraction integral. The effects of the near-field disturbances imposed by the shock on the image-plane irradiance patterns are investigated and the implications of these image-plane irradiance patterns on Shack-Hartmann wavefront sensor measurements are also discussed.

Obtaining high-resolution images using an optical microscope is critical when dealing with micro/nanoscale objects. Current techniques use high magnification objective lenses with high numerical apertures to resolve closely spaced objects at the micron/nanoscale. However, these lenses often require additional optics and have a narrow depth of field, preventing ease of use. To date, scanning electron microscopy (SEM) is used for imaging beyond the diffraction limit and has led to various breakthroughs in semiconductor physics and nanotechnology. An alternative to an SEM is using artificial intelligence (AI) to enable super-resolution techniques with correlated image sets. We utilize a convolutional neural network (CNN) and generative adversarial network (GAN) to train correlated images gathered from higher magnification SEM and lower magnification SEM, resulting in a model that enables resolving nanoscale features. We demonstrated that by training a neural network with SEM images, we are able to aid the optical microscope to image beyond the diffraction limit with a resolution closer to the SEM.

Fractal-based phase screens are compared to subharmonic-augmented FFT-based phase screens using both analytic and numerical statistical methods. Properties such as homogeneity and stationarity are investigated. It is shown analytically that augmented FFT-based screens are homogeneous and strict-sense stationary. Analytic means are also used to show that fractal-based phase screens are not stationary based on the definition of fractal Brownian surfaces. Corresponding numerical results show that the structure functions in both cases appear to be stationary or nearly so. It is shown that both types of phase screens must have “creasing” that has been observed in the power spectrum, due to edge effects. FFT-based screens without subharmonic augmentation, on the other hand, are shown to avoid such creasing. Sample results are also presented for imaging reconstructions with both types of screens.

A Semi-Analytical Angular Spectrum Method (SAASM) is introduced for the reconstruction process of lensless digital in-line holography with spherical wave illumination. This approach solves a distortion issue in the reconstructed field that emerges when the conventional, Angular Spectrum Method with planar reconstruction wave (ASM-PRW), method is used for high numerical aperture setups. The performance of the proposed approach is compared against ASM-PRW. The benefits of SAASM in terms of resolved limit and detection efficiency of small particles near the detection limit are shown using holograms containing simulated particles, suspended glass beams of known diameter and dry pollens.

This conference presentation was prepared for the Unconventional Imaging and Adaptive Optics 2022 conference at SPIE Optical Engineering + Applications, 2022.