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Collisional Broadening and Shift of Rubidium 4\u3csup\u3e2\u3c/sup\u3e\u3ci\u3eD\u3c/i\u3e\u3csub\u3e5/2\u3c/sub\u3e → \u3ci\u3en\u3csup\u3e2\u3c/sup\u3eF\u3c/i\u3e Line Shapes with Helium and Electrons
Get PDFLine shapes for three rubidium 42D5/2 → n2F transitions were collected using pump probe spectroscopy. Broadening rates for Rb-He collisions ranged from 139–171 MHz/Torr and shift rates ranged from 113–146 MHz/Torr. Then, line shapes were collected at different alkali densities to measure electron density. Electron density had a clear “runaway ionization density threshold” around 1.5×1013 cm−3. The three transitions agreed within 2% on the observed fractional ionization when calculated using the observed Stark width but did not agree on the observed fractional ionization when calculated using the observed Stark shift. These results indicate that Griem theory can provide reasonable estimates of Ne in an alkali plasma but can not reasonably estimate the observed shift at low buffer gas pressures
Evaluating Butterfly Orbits in the Earth-Moon Corridor for SSA, Lunar Surface Surveillance, and Cislunar Disposal
Get PDFThe increased space activity in the region of cislunar space has created a high demand to identify, track, and monitor Resident Space Objects (RSOs) and provide cislunar disposal routes for RSOs. This research investigates the practicality of various satellite maneuvers between orbits chosen from the butterfly and dragonfly orbit families. Several simulated trajectories are compared to show the feasibility of performing a maneuver in cislunar and lunar space between the L1 and L2 Lagrange points. Additionally, lunar surface space situational awareness (SSA) capabilities of northern and southern butterfly (BN/BS) orbits were evaluated and compared to provide the most favorable dynamic model for space-based lunar surveillance. This research also investigates the practicality of various trajectories for decommissioned satellites chosen from unstable manifolds in the BS orbit family. Gravitational effects from the Earth, Moon, and Sun are incorporated into several simulated trajectories in order to best evaluate their feasibility for cislunar disposal. Results of this research show that butterfly orbits can be highly effective for observing RSOs in or near a dragonfly orbit, with all scenarios providing at least 90% visibility year-round. Further analysis concludes that the butterfly orbit family is unfavorable for a primarily lunar surveillance mission. Additionally, various manifold-based trajectories originating from a butterfly orbit were found to be optimal for cislunar disposal, particularly for decommissioning satellites
Advancing Robust Autonomous System Localization: Labeling Optimizations for Convolutional Neural Networks
Get PDFAAR is increasingly critical as aircraft autonomy advances, particularly for the Global Strike mission of the USAF, enhancing operational range and endurance. Traditional methods relying on GPS and custom communication links are limited in GPS-denied environments. This dissertation advances a single camera method to estimate object pose across three interconnected studies. The system trains a CNN on synthetic imagery to predict bboxes for object components, Solve-PnP algorithm finds the 6DoF pose, then employs novel pseudo-labeling on real-world images. These findings are pivotal for the AAR community and contribute to robotics, computer vision, and CNN research. By enabling robust GPS-free autonomous systems, this research advances capabilities critical to future autonomous aerospace operations
Air Force Institute of Technology Research Report 2021
Get PDFThis report summarizes the research activities of the Air Force Institute of Technology\u27s Graduate School of Engineering and Management, as well as AFIT\u27s research centers. It describes research interests and faculty expertise; list student theses/dissertations; identifies research sponsors and contributions; and outlines the procedure for contacting entities within the Institution
Esoclinic subspaces, covers of the complete graph, and complex conference matrices
No full textIn 1992, Godsil and Hensel published a ground-breaking study of distance-regular antipodal covers of the complete graph that, among other things, introduced an important connection with equi-isoclinic subspaces. This connection seems to have been overlooked, as many of its immediate consequences have never been detailed in the literature. To correct this situation, we first describe how Godsil and Hensel\u27s machine uses representation theory to construct equi-isoclinic tight fusion frames. Applying this machine to Mathon\u27s construction produces ℝq+1 equi-isoclinic planes in Rq+1 for any even prime power q \u3e 2. Despite being an application of the 30-year-old Godsil–Hensel result, infinitely many of these parameters have never been enunciated in the literature. Following ideas from Et-Taoui, we then investigate a fruitful interplay with complex symmetric conference matrices
Laboratory Exercise for the Radiometry Student
Get PDFThe U.S. Air and Space Forces require optical expertise among their personnel. The Air Force Institute of Technology offers a graduate optics curriculum, which includes a three-course sequence to educate students in the optical concepts of radiometry and radiometric instrumentation. We find radiometry is often a deceptively difficult concept for students to master. To address this, we have developed an experiment in our optics-laboratory coursework to help them gain this mastery. A Fourier-transform infrared spectrometer (FTS) is used to collect spectral data from an unknown sample. FTS calibration and data collection are discussed here, as are the two specific samples used, one with specular reflectance properties, the other with diffuse. The analysis methodology used on the data is also discussed. This is a good radiometry exercise to reveal to the student what can be learned about an unknown material’s optical properties in a remote-sensing scenario and is the basis upon which the limiting simplifications of this initial experiment may be generalized to address more difficult, but more realistic, remote-sensing analyses
The Maximal Covering Location Disruption Problem
No full textThis research sets forth and examines a new sequential, competitive location problem. The maximal covering location disruption problem is a zero-sum Stackelberg game comprised of two stages. A leader denies access to at most q out of n possible facility locations in the first stage and, in the second stage, a follower solves a maximal covering location problem while emplacing at most p facilities. Identifying this problem as both relevant and unaddressed in the current literature, this research examines properties of the bilevel programming formulation to inform heuristic development, subsequently evaluating the efficacy and efficiency of two variants each of an iterative, bounding heuristic (IBH) and a reformulation-based construction heuristic (RCH) over a two sets collectively consisting of 2160 test instances representing a breadth of relative parametric values. Although we illustrate that each heuristic may not identify an optimal solution, computational testing demonstrates the superlative and generally excellent performance of the RCH variants. For the 12.4% of instances for which the RCH does not readily verify the optimality of its solution, lower-bounding procedures characterize solution quality. Both of the RCH variants attain solutions with an average 4.08% relative optimality gap, and they scaled well over different parametric value combinations, solving instances in an average of 98.0 and 123.6 seconds, respectively
A Novel Methodology for Gamma-Ray Spectra Dataset Procurement Over Varying Standoff Distances and Source Activities
No full textThe adoption of machine learning approaches for gamma-ray spectroscopy has received considerable attention in the literature. Many studies have investigated the deployment of various algorithm architectures to a specific task. However, little attention has been afforded to the development of the datasets leveraged to train the models. Such training datasets typically span a set of environmental or detector parameters to encompass a problem space of interest to a user. Variations in these measurement parameters will also induce fluctuations in the detector response, including pile-up and ground scatter effects. Fundamental to this work is the understanding that 1) the underlying spectral shape varies as the measurement parameters change and 2) the statistical uncertainties associated with two spectra impact their level of similarity. While previous studies attribute some arbitrary discretization to the measurement parameters for the generation of their synthetic training data, this work introduces a principled methodology for efficient spectral-based discretization of a problem space. A signal-to-noise ration (SNR) respective spectral comparison measure and a Gaussian Process Regression (GPR) mode are used to predict the spectral similarity across a range of measurement parameters. This innovative approach effectively showcased its capability by dividing problem space, ranging from 5cm to 100 cm standoff distances and 5 μCi–100 μCi of Cs, into three unique combinations of measurement parameters. The findings from this work will aid in creating more robust datasets, which incorporate many possible measurement scenarios, reduce the number of required experimental test set measurements, and possibly enable experimental training data collection for gamma-ray spectroscopy
Equi-isoclinic subspaces from symmetry
No full textWe describe a flexible technique that constructs tight fusion frames with prescribed transitive symmetry. Applying this technique with representations of the symmetric and alternating groups, we obtain several new infinite families of equi-isoclinic tight fusion frames, each with the remarkable property that its automorphism group is either Sn or An. These ensembles are optimal packings for Grassmannian space equipped with spectral distance, and as such, they find applications in block compressed sensing
A Machine Learning Approach for Multipath Characterization and Mitigation Using Chipshape Observations
No full textMultipath continues to be a significant error source in satellite navigation. Recent solutions with Neural Networks (NN) model the effects of multipath on the autocorrelation function. Chipshape correlation provides a detailed look into the spreading code transitions in the time domain. It is useful in applications such as Signal Quality Monitoring (SQM) and is much more sensitive to multipath than autocorrelation. This research proposes NN models that each predict pseudorange or carrier range errors due to multipath by monitoring the chipshape correlation output. The code range model makes predictions for a simulation with 50 MHz precorrelation bandwidth and one multipath ray 3 dB below the line-of-sight (LOS) and a noise floor 14 dB above the LOS with an average of ±0.82 meters error, the carrier range model makes predictions with an average of ±5.63e-4 meters error. These models were accurate at predicting range errors for static multipath, however, the code range model is sensitive to the motion profile of the multipath ray relative to the LOS source