Other Aerospace Engineering Commons^™

Seizing Strategic High Ground Of Space Computing Power: Global Competition Landscape And China’S Path, Yan Chen, Wenbin Song, Ping Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The deep integration of artificial intelligence and commercial aerospace is accelerating the transformation of space computing power from conceptual exploration to engineering verification, becoming a key direction for building an integrated space-air-ground information infrastructure. This study delves into its strategic value, global landscape, industrial chain bottlenecks, and advancement paths. The research reveals that the core value of space computing power does not lie in replacing ground data centers, but rather in focusing on network coverage blind spots, data transmission limitations, and high-timeliness scenarios, providing a new supply model of “in-orbit computing + space-ground collaboration”. Currently, the world has entered a …

Adaptive Artificial Potential Field Guidance And Control For Autonomous Docking With Uncooperative And Unknown Spacecraft, Steven Holmberg

Theses and Dissertations

The increasing demand for on-orbit servicing (OOS), active debris removal (ADR), and space domain awareness (SDA) missions has increased the need for autonomous spacecraft rendezvous and proximity operations (RPO) with uncooperative and unknown targets. Traditional guidance and control methods are typically designed for cooperative systems with known geometry and state information. This work builds on previous research to develop and evaluate an artificial potential field (APF)-based control framework capable of autonomous operation with minimal prior target knowledge and applicability to both relatively static and tumbling spacecraft.

The proposed APF formulation incorporates established safety constructs from cooperative docking systems, including an …

Unresolved Image Simulation For Space Situational Awareness Applications, Fox Coniglario

Doctoral Dissertations and Master's Theses

The knowledge of what lies in orbit around Earth is at best a guess. Decades of spaceflight, debris buildup, and vehicle collisions have contributed to a large number of objects that are simply not able to be catalogued. Ongoing efforts to catalog debris in orbit have reached limits by conventional measures and as such, research is active in the field of in-orbit space situational awareness. This thesis intends to help fill a hole in the development of such orbital platforms by assisting the development of image processing software pipelines though the simulation of unresolved space imagery. The simulation uses accurate …

Advancing Real-World Implementation Of The Well Optimized Linear Finder (Wolf) High-Speed Atmospheric Turbulence Compensation Method, Timothy Evan Coon

Theses and Dissertations

This dissertation advances the real-world implementation of the Well Optimized Linear Finder (WOLF) method for high-speed Atmospheric Turbulence Compensation (ATC). Atmospheric turbulence introduces phase aberrations into optical wavefronts and degrades image quality in terrestrial imaging systems. Traditional phase diversity methods are computationally intensive and poorly suited to real-time operation. The WOLF method addresses these limitations through a novel, point-wise formulation of the optical transfer function (OTF) as a structured autocorrelation of the generalized pupil function (GPF). This formulation enables the estimation of phase aberrations at individual spatial coordinates with distributed computational complexity.

The research begins by developing a MATLAB-based simulation …

Reconfigurable Python Autopilot Software For Rc Aircraft, Kate Doiron

Honors Theses

No abstract provided.

Machine Learning Methods For Hypervelocity Fragment Flyout Characterization, Katharine Larsen

Doctoral Dissertations and Master's Theses

Resulting from breakup events, such as collisions and explosions, hypervelocity fragments create potential hazards for both terrestrial and on-orbit environments, such as terrestrial weapons explosions and satellite breakup events, respectively. To avoid unnecessary damage, an accurate understanding or characterization of hypervelocity fragmentation events is vital. Currently, publicly available two-line elements collected from on-orbit breakup events are limited, excluding pre-detonation parent body conditions, such as orientation, and information of smaller fragments. The uncertainty of these datasets varies between each collected set. Therefore, the overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris …

Lethality And Survivability Of Autonomous Self-Sensing Uavs, Jeffrey T. Wilson

Theses and Dissertations

Unmanned Aerial Vehicles (UAVs) have seen increased usage over the past two decades during the Global War on Terrorism (GWOT), operating in low-risk environments against dispersed enemies with minimal counter-drone capabilities. However, as the U.S. military shifts focus to Multi-Domain Operations (MDO) and Large Scale Combat Operations (LSCO), UAVs face significantly higher risks, including frequent and successful attacks, as well as the exploitation of their technology. Battle damage assessment (BDA) is not new; however, autonomous self-assessment by UAVs represents a novel advancement. Currently, UAV BDA relies on manual inspection, requiring approximately eight hours per drone. By adopting self-sensing technology, UAVs …

Choosing Robust Leadership: Encompassing The Best-Of-N Model And Swarm Intelligence Optimization For Heterogeneous Multiple Autonomous Unmanned Aerial Vehicle Systems, Kudamuhandiramlage Harith Kolitha Warnakulasooriya

Shelby Hall Graduate Research Forum Presentations

Presentation slides for a presentation given at the 1st annual Shelby Hall Graduate Research Forum at the University of South Alabama.

Fusion-Based Utilization And Synthesis Of Efficient Detections, Ethan C. Rogers, Parker H. Liberatore, Thomas G. James

Endeavors: Mississippi State Undergraduate Research Journal

This study aimed to develop hardware and software for an object detection fusion system, using three different sensors. The system was built and studied with the motivating application of autonomous drones searching for and detecting people in a search-and-rescue scenario. The system’s performance was compared to that of individual sensors deployed for the same task. The focus of the research was to prove the competence and benefits of a decision-level fusion method as it was applied to a lightweight object detection architecture, and the driving motivators behind the study were simplicity in implementation and good computational performance. In short, the …

Development Of User Interface And Testing Harness, Jacob Amezquita, William Albertini

College of Engineering Summer Undergraduate Research Program

No abstract provided.

A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu, Irene Moulitsas

Journal of Aviation/Aerospace Education & Research

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …

Machine Learning To Predict Warhead Fragmentation In-Flight Behavior From Static Data, Katharine Larsen

Doctoral Dissertations and Master's Theses

Accurate characterization of fragment fly-out properties from high-speed warhead detonations is essential for estimation of collateral damage and lethality for a given weapon. Real warhead dynamic detonation tests are rare, costly, and often unrealizable with current technology, leaving fragmentation experiments limited to static arena tests and numerical simulations. Stereoscopic imaging techniques can now provide static arena tests with time-dependent tracks of individual fragments, each with characteristics such as fragment IDs and their respective position vector. Simulation methods can account for the dynamic case but can exclude relevant dynamics experienced in real-life warhead detonations. This research leverages machine learning methodologies to …

Soarnet, Deep Learning Thermal Detection For Free Flight, Jake T. Tallman

Master's Theses

Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not …

Electro-Optic Satellite Constellation Design Using Multi-Objective Genetic Algorithm, Yasin Tamer

Theses and Dissertations

Satellite constellation design is a complex, highly constrained, and multidisciplinary problem. Unless optimization tools are used, tradeoffs must be conducted at the subsystem level resulting in feasible, but not necessarily optimal, system designs. As satellite technology advances, new methods to optimize the system objectives are developed. This study is based on the development of a representative regional remote sensing constellation design. This thesis analyses the design process of an electrooptic satellite constellation with regional coverage considerations using system-level optimization tools. A multi objective genetic algorithm method is used to optimize the constellation design by utilizing MATLAB and STK integration. Cost, …

Satellite Constellation Deployment And Management, Joseph Ryan Kopacz

Electronic Theses and Dissertations

This paper will review results and discuss a new method to address the deployment and management of a satellite constellation. The first two chapters will explorer the use of small satellites, and some of the advances in technology that have enabled small spacecraft to maintain modern performance requirements in incredibly small packages.

The third chapter will address the multiple-objective optimization problem for a global persistent coverage constellation of communications spacecraft in Low Earth Orbit. A genetic algorithm was implemented in MATLAB to explore the design space – 288 trillion possibilities – utilizing the Satellite Tool Kit (STK) software developers kit. …

Autonomous Satellite Operations For Cubesat Satellites, Jason Lionel Anderson

Master's Theses

In the world of educational satellites, student teams manually conduct operations daily, sending commands and collecting downlinked data. Educational satellites typically travel in a Low Earth Orbit allowing line of sight communication for approximately thirty minutes each day. This is manageable for student teams as the required manpower is minimal. The international Global Educational Network for Satellite Operations (GENSO), however, promises satellite contact upwards of sixteen hours per day by connecting earth stations all over the world through the Internet. This dramatic increase in satellite communication time is unreasonable for student teams to conduct manual operations and alternatives must be …

Multi -Mission Attitude Determination System For Balloon Flight, Liping Mo

Doctoral Dissertations

MADS (Multi-mission Attitude Determination System) is a new software package used to determine the attitude of instruments on a high-altitude balloon employed for scientific experiments. There is no existing system for the automated determination of the attitude of instruments in balloon experiments, so we have developed MADS to do the data analysis for balloon experiments to find the location of astrophysical sources such as gamma-ray or x-ray sources.

The two areas that required most work were modeling star trackers and modeling the motion of the balloon. Star trackers are used on satellites, but are far too expensive and sophisticated to …