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Air Force Institute of Technology

Theses and Dissertations

2020

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Full-Text Articles in Physical Sciences and Mathematics

Deep Learning-Based, Passive Fault Tolerant Control Facilitated By A Taxonomy Of Cyber-Attack Effects, Dean C. Wardell Dec 2020

Deep Learning-Based, Passive Fault Tolerant Control Facilitated By A Taxonomy Of Cyber-Attack Effects, Dean C. Wardell

Theses and Dissertations

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control …

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Spectroscopic Diagnostics For Supersonic Air Microwave Discharges, James E. Caplinger Dec 2020

Spectroscopic Diagnostics For Supersonic Air Microwave Discharges, James E. Caplinger

Theses and Dissertations

Optical Emission Spectroscopy (OES) is an increasingly relevant technique in plasma diagnostics due to its inherent non-invasive nature and simple application relative to other popular techniques. In this work, common OES techniques are combined with novel methods, developed here, in an effort to provide comprehensive OES techniques for stationary and supersonic air microwave discharges. To this end, a detailed collisional-radiative model for strong atomic oxygen lines has been developed and used to identify the importance of often overlooked mechanisms including cascade emission and metastable excitation. Using these results, a combined argon actinometry technique was developed which makes use of the …

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Electro-Optic Satellite Constellation Design Using Multi-Objective Genetic Algorithm, Yasin Tamer Dec 2020

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, …

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Analytic Provenance For Software Reverse Engineers, Wayne C. Henry Sep 2020

Analytic Provenance For Software Reverse Engineers, Wayne C. Henry

Theses and Dissertations

Reverse engineering is a time-consuming process essential to software-security tasks such as malware analysis and vulnerability discovery. During the process, an engineer will follow multiple leads to determine how the software functions. The combination of time and possible explanations makes it difficult for the engineers to maintain a context of their findings within the overall task. Analytic provenance tools have demonstrated value in similarly complex fields that require open-ended exploration and hypothesis vetting. However, they have not been explored in the reverse engineering domain. This dissertation presents SensorRE, the first analytic provenance tool designed to support software reverse engineers. A …

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Direct Digital Synthesis: A Flexible Architecture For Advanced Signals Research For Future Satellite Navigation Payloads, Pranav R. Patel Sep 2020

Direct Digital Synthesis: A Flexible Architecture For Advanced Signals Research For Future Satellite Navigation Payloads, Pranav R. Patel

Theses and Dissertations

In legacy Global Positioning System (GPS) Satellite Navigation (SatNav) payloads, the architecture does not provide the flexibility to adapt to changing circumstances and environments. GPS SatNav payloads have largely remained unchanged since the system became fully operational in April 1995. Since then, the use of GPS has become ubiquitous in our day-to-day lives. GPS availability is now a basic assumption for distributed infrastructure; it has become inextricably tied to our national power grids, cellular networks, and global financial systems. Emerging advancements of easy to use radio technologies, such as software-defined radios (SDRs), have greatly lowered the difficulty of discovery and …

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Physics-Constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios, Nicholas M. Westing Sep 2020

Physics-Constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios, Nicholas M. Westing

Theses and Dissertations

Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using …

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Improving Closely Spaced Dim Object Detection Through Improved Multiframe Blind Deconvolution, Ronald M. Aung Sep 2020

Improving Closely Spaced Dim Object Detection Through Improved Multiframe Blind Deconvolution, Ronald M. Aung

Theses and Dissertations

This dissertation focuses on improving the ability to detect dim stellar objects that are in close proximity to a bright one, through statistical image processing using short exposure images. The goal is to improve the space domain awareness capabilities with the existing infrastructure. Two new algorithms are developed. The first one is through the Neighborhood System Blind Deconvolution where the data functions are separated into the bright object, the neighborhood system, and the background functions. The second one is through the Dimension Reduction Blind Deconvolution, where the object function is represented by the product of two matrices. Both are designed …

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A Methodology To Identify Alternative Suitable Nosql Data Models Via Observation Of Relational Database Interactions, Paul M. Beach Sep 2020

A Methodology To Identify Alternative Suitable Nosql Data Models Via Observation Of Relational Database Interactions, Paul M. Beach

Theses and Dissertations

The effectiveness and performance of data-intensive applications are influenced by the suitability of the data models upon which they are built. The relational data model has been the de facto data model underlying most database systems since the 1970’s. However, the recent emergence of NoSQL data models have provided users with alternative ways of storing and manipulating data. Previous research has demonstrated the potential value in applying NoSQL data models in non-distributed environments. However, knowing when to apply these data models has generally required inputs from system subject matter experts to make this determination. This research, sponsored by the Air …

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Joint 1d And 2d Neural Networks For Automatic Modulation Recognition, Luis M. Rosario Morel Sep 2020

Joint 1d And 2d Neural Networks For Automatic Modulation Recognition, Luis M. Rosario Morel

Theses and Dissertations

The digital communication and radar community has recently manifested more interest in using data-driven approaches for tasks such as modulation recognition, channel estimation and distortion correction. In this research we seek to apply an object detector for parameter estimation to perform waveform separation in the time and frequency domain prior to classification. This enables the full automation of detecting and classifying simultaneously occurring waveforms. We leverage a lD ResNet implemented by O'Shea et al. in [1] and the YOLO v3 object detector designed by Redmon et al. in [2]. We conducted an in depth study of the performance of these …

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Low-Information Radiation Imaging Using Rotating Scatter Mask Systems And Neural Network Algorithms, Robert J. Olesen Sep 2020

Low-Information Radiation Imaging Using Rotating Scatter Mask Systems And Neural Network Algorithms, Robert J. Olesen

Theses and Dissertations

While recent studies have demonstrated the directional capabilities of the single-detector rotating scatter mask (RSM) system for discrete, dual-particle environments, there has been little progress towards adapting it as a true imaging device. In this research, two algorithms were developed and tested using an RSM mask design previously optimized for directional detection and simulated 137Cs signals from a variety of source distributions. The first, maximum-likelihood expectation-maximization (ML-EM), was shown to generate noisy images, with relatively low accuracy (145% average relative error) and signal-to-noise ratio (0.27) for most source distributions simulated. The second, a novel regenerative neural network (ReGeNN), performed exceptionally …

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Artificial Intelligence In Pursuit-Evasion Games, Specifically In The Scotland Yard Game, Arif M. Alamri Sep 2020

Artificial Intelligence In Pursuit-Evasion Games, Specifically In The Scotland Yard Game, Arif M. Alamri

Theses and Dissertations

This research provides a heuristic algorithm for the detectives, who try to collectively capture a criminal known as Mr. X, in the Scotland Yard pursuer-evasion game. In Scotland Yard, a team of detectives attempts to converge on and capture a criminal known as Mr. X. The heuristic algorithm developed in this thesis is designed to emulate human strategies when playing the game. The algorithm uses the current state of the board at each time step, including the current positions of the detectives as well as the last known position of Mr. X. The heuristic algorithm then analyses all of the …

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Chronos Spacecraft With Chiron Probe: Exploration Of The Hydrosphere, Principle Satellites, Atmosphere, And Rings Of Uranus, Payton E. Pearson Sep 2020

Chronos Spacecraft With Chiron Probe: Exploration Of The Hydrosphere, Principle Satellites, Atmosphere, And Rings Of Uranus, Payton E. Pearson

Theses and Dissertations

A design reference mission using more modern technological innovations has been developed for exploration of the outer reaches of our Solar System, specifically Uranus and its system of satellites. This mission will utilize theoretical technologies mostly without regard to their current technological readiness level (TRL), though most systems have a TRL of at least 5. The primary innovations explored in this thesis are the new launch systems that provide far greater payload capacity potentially sent to anywhere in the Solar System, new Stirling-engine radioisotope thermoelectric generators (SRTGs), vastly improved data storage technologies, optimized satellite antenna relay of data using much …

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Simulated Experince Evaluation In Developing Multi-Agent Coordination Graphs, Andrew J. Watson Jul 2020

Simulated Experince Evaluation In Developing Multi-Agent Coordination Graphs, Andrew J. Watson

Theses and Dissertations

Cognitive science has proposed that a way people learn is through self-critiquing by generating 'what-if' strategies for events (simulation). It is theorized that people use this method to learn something new as well as to learn more quickly. This research adds this concept to a graph-based genetic program. Memories are recorded during fitness assessment and retained in a global memory bank based on the magnitude of change in the agent’s energy and age of the memory. Between generations, candidate agents perform in simulations of the stored memories. Candidates that perform similarly to good memories and differently from bad memories are …

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Monte Carlo Tree Search Applied To A Modified Pursuit/Evasion Scotland Yard Game With Rendezvous Spaceflight Operation Applications, Joshua A. Daughtery Jun 2020

Monte Carlo Tree Search Applied To A Modified Pursuit/Evasion Scotland Yard Game With Rendezvous Spaceflight Operation Applications, Joshua A. Daughtery

Theses and Dissertations

This thesis takes the Scotland Yard board game and modifies its rules to mimic important aspects of space in order to facilitate the creation of artificial intelligence for space asset pursuit/evasion scenarios. Space has become a physical warfighting domain. To combat threats, an understanding of the tactics, techniques, and procedures must be captured and studied. Games and simulations are effective tools to capture data lacking historical context. Artificial intelligence and machine learning models can use simulations to develop proper defensive and offensive tactics, techniques, and procedures capable of protecting systems against potential threats. Monte Carlo Tree Search is a bandit-based …

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Neural Network Models For Nuclear Treaty Monitoring: Enhancing The Seismic Signal Pipeline With Deep Temporal Convolution, Joshua T. Dickey Jun 2020

Neural Network Models For Nuclear Treaty Monitoring: Enhancing The Seismic Signal Pipeline With Deep Temporal Convolution, Joshua T. Dickey

Theses and Dissertations

Seismic signal processing at the IDC is critical to global security, facilitating the detection and identification of covert nuclear tests in near-real time. This dissertation details three research studies providing substantial enhancements to this pipeline. Study 1 focuses on signal detection, employing a TCN architecture directly against raw real-time data streams and effecting a 4 dB increase in detector sensitivity over the latest operational methods. Study 2 focuses on both event association and source discrimination, utilizing a TCN-based triplet network to extract source-specific features from three-component seismograms, and providing both a complimentary validation measure for event association and a one-shot …

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Design And Test Of An Autonomy Monitoring Service To Detect Divergent Behaviors On Unmanned Aerial Systems, Loay Y. Almannaei Jun 2020

Design And Test Of An Autonomy Monitoring Service To Detect Divergent Behaviors On Unmanned Aerial Systems, Loay Y. Almannaei

Theses and Dissertations

Operation of Unmanned Aerial Vehicles (UAV) support many critical missions in the United State Air Force (USAF). Monitoring abnormal behavior is one of many responsibilities of the operator during a mission. Some behaviors are hard to be detect by an operator, especially when flying one or more autonomous vehicles; as such, detections require a high level of attention and focus to flight parameters. In this research, a monitoring system and its algorithm are designed and tested for a target fixed-wing UAV. The Autonomy Monitoring Service (AMS) compares the real vehicle or simulated Vehicle with a similar simulated vehicle using Software …

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Conceptualization And Application Of Deep Learning And Applied Statistics For Flight Plan Recommendation, Nicholas C. Forrest Mar 2020

Conceptualization And Application Of Deep Learning And Applied Statistics For Flight Plan Recommendation, Nicholas C. Forrest

Theses and Dissertations

The Air Forces Pilot Training Next (PTN) program seeks a more efficient pilot training environment emphasizing the use of virtual reality flight simulators alongside periodic real aircraft experience. The PTN program wants to accelerate the training pace and progress in undergraduate pilot training compared to traditional undergraduate pilot training. Currently, instructor pilots spend excessive time planning and scheduling flights. This research focuses on methods to auto-generate the planning of in-flight events using hybrid filtering and deep learning techniques. The resulting approach captures temporal trends of user-specific and program-wide student performance to recommend a feasible set of graded flight events for …

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A General Methodology To Optimize And Benchmark Edge Devices, Kyle J. Smathers Mar 2020

A General Methodology To Optimize And Benchmark Edge Devices, Kyle J. Smathers

Theses and Dissertations

The explosion of Internet Of Things (IoT), embedded and “smart” devices has also seen the addition of “general purpose” single board computers also referred to as “edge devices.” Determining if one of these generic devices meets the need of a new given task however can be challenging. Software generically written to be portable or plug and play may be too bloated to work properly without significant modification due to much tighter hardware resources. Previous work in this area has been focused on micro or chip-level benchmarking which is mainly useful for chip designers or low level system integrators. A higher …

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Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee Mar 2020

Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee

Theses and Dissertations

The 45th Weather Squadron supports the space launch efforts out of the Kennedy Space Center and Cape Canaveral Air Force Station for the Department of Defense, NASA, and commercial customers through weather assessments. Their assessment of the Lightning Launch Commit Criteria (LLCC) for avoidance of natural and rocket triggered lightning to launch vehicles is critical in approving space shuttle and rocket launches. The LLCC includes standards for cloud formations, which requires proper cloud identification and characterization methods. Accurate reflectivity measurements for ground weather radar are important to meet the LLCC for rocket triggered lightning. Current linear interpolation methods for ground …

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Modeling Nonlinear Heat Transfer For A Pin-On-Disc Sliding System, Brian A. Boardman Mar 2020

Modeling Nonlinear Heat Transfer For A Pin-On-Disc Sliding System, Brian A. Boardman

Theses and Dissertations

The objective of this research is to develop a numerical method to characterize heat transfer and wear rates for samples of Vascomax® 300, or Maraging 300, steel. A pin-on-disc experiment was conducted in which samples were exposed to a high-pressure, high-speed, sliding contact environment. This sliding contact generates frictional heating that influences the temperature distribution and wear characteristics of the test samples. A two-dimensional nonlinear heat transfer equation is discretized and solved via a second-order explicit finite difference scheme to predict the transient temperature distribution of the pin. This schematic is used to predict the removal of material from the …

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Object Detection With Deep Learning To Accelerate Pose Estimation For Automated Aerial Refueling, Andrew T. Lee Mar 2020

Object Detection With Deep Learning To Accelerate Pose Estimation For Automated Aerial Refueling, Andrew T. Lee

Theses and Dissertations

Remotely piloted aircraft (RPAs) cannot currently refuel during flight because the latency between the pilot and the aircraft is too great to safely perform aerial refueling maneuvers. However, an AAR system removes this limitation by allowing the tanker to directly control the RP A. The tanker quickly finding the relative position and orientation (pose) of the approaching aircraft is the first step to create an AAR system. Previous work at AFIT demonstrates that stereo camera systems provide robust pose estimation capability. This thesis first extends that work by examining the effects of the cameras' resolution on the quality of pose …

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Applying Data Organizational Techniques To Enhance Air Force Learning, Jacob A. Orner Mar 2020

Applying Data Organizational Techniques To Enhance Air Force Learning, Jacob A. Orner

Theses and Dissertations

The USAF and the DoD use traditional schoolhouses to educate and train personnel. The physical aspects of these schoolhouses limit throughput. A method to increase throughput is to shift towards an asynchronous learning environment where students move through content at individually. This research introduces a methodology for transforming a set of unstructured documents into an organized TM students can use to orient themselves in a domain. The research identifies learning paths within the TM to create a directed KSAT. We apply this methodology in four case studies, each an education or training course. Using a graph comparison metric and the …

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Validation Technique For Modeled Bottomside Ionospheres Via Ray Tracing, Kevin S. Burg Mar 2020

Validation Technique For Modeled Bottomside Ionospheres Via Ray Tracing, Kevin S. Burg

Theses and Dissertations

A new method for validating ionosphere models using High Frequency (HF) angle of arrival (AoA) data is presented. AoA measurements from a field campaign held at White Sands Missile Range, New Mexico, USA in January 2014 provide the actual elevation angle, azimuth and group delay results from 10 transmitter-receiver circuits. Simulated AoAs are calculated by ray tracing through the electron density profiles predicted from the ionosphere models hosted by NASA's Community Coordinated Modeling Center: IRI-2016, USU-GAIM, GITM, CTIPe, TIE-GCM, and SAMI3. Through the implementation of metrics including Mean Absolute Error, Prediction Efficiency, Correlation Coefficient, and others, we are able to …

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One-Dimensional Multi-Frame Blind Deconvolution Using Astronomical Data For Spatially Separable Objects, Marc R. Brown Mar 2020

One-Dimensional Multi-Frame Blind Deconvolution Using Astronomical Data For Spatially Separable Objects, Marc R. Brown

Theses and Dissertations

Blind deconvolution is used to complete missions to detect adversary assets in space and to defend the nation's assets. A new algorithm was developed to perform blind deconvolution for objects that are spatially separable using multiple frames of data. This new one-dimensional approach uses the expectation-maximization algorithm to blindly deconvolve spatially separable objects. This object separation reduces the size of the object matrix from an NxN matrix to two singular vectors of length N. With limited knowledge of the object and point spread function the one-dimensional algorithm successfully deconvolved the objects in both simulated and laboratory data.

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Developing A Serious Game To Explore Joint All Domain Command And Control, Nathaniel W. Flack Mar 2020

Developing A Serious Game To Explore Joint All Domain Command And Control, Nathaniel W. Flack

Theses and Dissertations

Changes in the geopolitical landscape and increasing technological complexity have prompted the U.S. Military to coin Multi-Domain Operations (MDO) and Joint All-Domain Command and Control as terms to describe an over-arching strategy that frames the complexity of warfare across both traditional and emerging warfighting domains. Teaching new and advanced concepts associated with these terms requires both innovation as well as distinct education and training tools in order to realize the cultural change advocated by senior military leaders. BSN, a Collectible Card Game, was developed to teach concepts integral to MDO and initiate discussion on military strategy.

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Event-Based Visual-Inertial Odometry Using Smart Features, Zachary P. Friedel Mar 2020

Event-Based Visual-Inertial Odometry Using Smart Features, Zachary P. Friedel

Theses and Dissertations

Event-based cameras are a novel type of visual sensor that operate under a unique paradigm, providing asynchronous data on the log-level changes in light intensity for individual pixels. This hardware-level approach to change detection allows these cameras to achieve ultra-wide dynamic range and high temporal resolution. Furthermore, the advent of convolutional neural networks (CNNs) has led to state-of-the-art navigation solutions that now rival or even surpass human engineered algorithms. The advantages offered by event cameras and CNNs make them excellent tools for visual odometry (VO). This document presents the implementation of a CNN trained to detect and describe features within …

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Cyber-Physical System Intrusion: A Case Study Of Automobile Identification Vulnerabilities And Automated Approaches For Intrusion Detection, David R. Crow Mar 2020

Cyber-Physical System Intrusion: A Case Study Of Automobile Identification Vulnerabilities And Automated Approaches For Intrusion Detection, David R. Crow

Theses and Dissertations

Today's vehicle manufacturers do not tend to publish proprietary packet formats for the controller area network (CAN), a network protocol regularly used in automobiles and manufacturing. This is a form of security through obscurity -it makes reverse engineering efforts more difficult for would-be intruders -but obfuscating the CAN data in this way does not adequately hide the vehicle's unique signature, even if these data are unprocessed or limited in scope. To prove this, we train two distinct deep learning models on data from 11 different vehicles. Our results clearly indicate that one can determine which vehicle generated a given sample …

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Comparison Of Visual Simultaneous Localization And Mapping Methods For Fixed-Wing Aircraft Using Slambench2, Patrick R. Latcham Mar 2020

Comparison Of Visual Simultaneous Localization And Mapping Methods For Fixed-Wing Aircraft Using Slambench2, Patrick R. Latcham

Theses and Dissertations

Visual Simultaneous Localization and Mapping (VSLAM) algorithms have evolved rapidly in the last few years, however there has been little research evaluating current algorithm's effectiveness and limitations when applied to tracking the position of a fixed-wing aerial vehicle. This research looks to evaluate current monocular VSLAM algorithms' performance on aerial vehicle datasets using the SLAMBench2 benchmarking suite. The algorithms tested are MonoSLAM, PTAM, OKVIS, LSDSLAM, ORB-SLAM2, and SVO, all of which are built into the SLAMBench2 software. The algorithms' performance is evaluated using simulated datasets generated in the AftrBurner Engine. The datasets were designed to test the quality of each …

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Honeyhive - A Network Intrusion Detection System Framework Utilizing Distributed Internet Of Things Honeypot Sensors, Zachary D. Madison Mar 2020

Honeyhive - A Network Intrusion Detection System Framework Utilizing Distributed Internet Of Things Honeypot Sensors, Zachary D. Madison

Theses and Dissertations

Exploding over the past decade, the number of Internet of Things (IoT) devices connected to the Internet jumped from 3.8 billion in 2015 to 17.8 billion in 2018. Because so many IoT devices remain upatched, unmonitored, and left on, they have become a tantalizing target for attackers to gain network access or add another device to their botnet. HoneyHive is a framework that uses distributed IoT honeypots as Network Intrusion Detection Systems (NIDS) sensors that beacon back to a centralized Command and Control (C2) server. The tests in this experiment involve four types of scans and four levels of active …

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Sliver: Simulation-Based Logic Bomb Identification/Verification For Unmanned Aerial Vehicles, Jake M. Magness Mar 2020

Sliver: Simulation-Based Logic Bomb Identification/Verification For Unmanned Aerial Vehicles, Jake M. Magness

Theses and Dissertations

This research introduces SLIVer, a Simulation-based Logic Bomb Identification/Verification methodology, for finding logic bombs hidden within Unmanned Aerial Vehicle (UAV) autopilot code without having access to the device source code. Effectiveness is demonstrated by executing a series of test missions within a high-fidelity software-in-the-loop (SITL) simulator. In the event that a logic bomb is not detected, this methodology defines safe operating areas for UAVs to ensure to a high degree of confidence the UAV operates normally on the defined flight plan. SLIVer uses preplanned flight paths as the baseline input space, greatly reducing the input space that must be searched …

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