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Daniel Guggenheim School of Aerospace Engineering
Daniel Guggenheim School of Aerospace Engineering
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ItemA scalable hardware-in-the-Loop simulation for satellite constellations and other multi-agent networks(Georgia Institute of Technology, 2018-05-01) DeGraw, Christopher F.
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ItemA scalable hardware-in-the-Loop simulation for satellite constellations and other multi-agent networks(Georgia Institute of Technology, 2018-04-30) Degraw, Christopher F.Given the plans for satellite mega-constellations, there is a lack of rigorously tested operations and control methods for constellations larger than 30 to 50 spacecraft. The purpose of this thesis is to propose the principles behind a robust, modular, and scalable system able to provide software-in-the-loop (SWIL) and hardware-in-the-loop (HWIL) simulation capabilities for the advancement of formation and constellation system Technology Readiness Levels (TRL). Additionally, this thesis will develop a first generation system demonstrating these principles called Constellation Simulation on a Massive Scale, or COSMoS. The preliminary goals of COSMoS are to 1) simulate multiple or more satellites in a constellation to demonstrate scalable capability; and 2) connect to external hardware devices in real-time to demonstrate HWIL capability. The simulation framework behind COSMoS is the Multi-Agent Distributed Network Simulator, or MADNS. MADNS is a real-time hardware-in-the-loop (RT-HWIL) simulator capable of communicating with independent agents and external hardware and software elements. This framework will encapsulate the COSMoS simulation but will be designed to work with any multi-agent network simulation designed within the constraints of the MADNS API. This thesis will show the results of the preliminary development of both MADNS and COSMoS and will present a direction for the further development of both a satellite constellation simulator and general real-time hardware-in-the-loop simulators.
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ItemEnhanced flight vision systems: Portrayal of runway markings and sensor range effects on pilot performance(Georgia Institute of Technology, 2018-04-26) Greenhill, AndrewThis thesis investigates the effects of two specific sensor limitations in enhanced flight vision systems (EFVS) on general aviation pilot performance during approach and landing: sensor range and EFVS portrayal of runway markings. The background section of this thesis describes current sensor technologies with EFVS: millimeter wave radar, forward-looking infrared, and light detection and ranging (LiDAR). In addition, the connections between pilot tasks, information requirements, visual cues and information processing level are identified. These connections show how limitations of sensor technologies could affect pilot performance. These effects were then assessed in a fixed base flight simulator of a general aviation aircraft with an EFVS system. The sensor range and portrayal of runway markings was varied while measuring pilot performance. Pilot performance during approach was measured according to FAA instrument certification standards. Landing performance was measured using standards taught during private pilot training. The results show that pilot performance in tracking an instrument approach is negatively affected by reductions in EFVS sensor range, while the vertical speed and distance from centerline had exceedances beyond acceptable standards when the EFVS did not portray runway markings. These results identify the key minimum specifications of EFVS sensor range and ability to portray runway markings for their implementation in general aviation.
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ItemPredictive sensor tasking and decision support for space situational awareness using evidential reasoning(Georgia Institute of Technology, 2018-04-06) Jaunzemis, Andris DavisSituation awareness is the perception of elements in the environment, comprehension of their meaning, and projection of their status into the future. Space situational awareness (SSA) is particularly concerned with accurately representing state knowledge of space objects to resolve potential threats, such as collision. Tracking techniques used in the space surveillance system still rely largely on models and applications from the 1950s and 1960s, while the number of tracked objects continues to grow with improved sensor technologies and ease-of-access to space. This work frames the SSA sensor tasking problem to interrogate specific hypotheses using evidential reasoning. First, cognitive systems engineering practices are applied to derive cognitive work and information relationship requirements for SSA decision-support systems and provide insight on the utility of hypothesis-based methods in SSA. To evaluate hypothesis-based methods for SSA, the spacecraft anomaly detection problem is formulated as a binary hypothesis test using distance metrics while accounting for non-Gaussian boundary conditions to improve applicability to non-linear orbital dynamics. Next, a sensor tasking criterion is developed to gather the evidence that minimizes ambiguity, or ignorance, in hypothesis resolution. The application of evidential reasoning provides a rigorous framework for quantifying ambiguity and allows inclusion of diverse SSA sensors. Building upon this method, a generalized evidence-gathering framework, Judicial Evidential Reasoning (JER), is proposed for hypothesis resolution tasks. JER also accounts for confirmation bias by applying a principle of equal effort. Resource allocation is a non-linear, high-dimensional, mixed-integer problem, so JER also applies adversarial optimization techniques to address computational tractability concerns. Finally, a prototype SSA decision support system is developed based on the derived requirements to evaluate workload and situation awareness impacts of hypothesis-based tasking. This work aims to enable predictive sensor tasking to provide decision-quality information and improve decision-maker situation awareness and workload.
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ItemIntegrated tasking, processing, and orbit determination for optical sensors in a space situational awareness framework(Georgia Institute of Technology, 2018-04-06) Murphy, Timothy S.This dissertation explores the following. Coordinating a large network of electo-optical sensors (EOS) for an effective Space Surveillance Network requires several novel capabilities. The first piece of this work involves the search set, or the set of orbits which may contain relevant object(s). A tasking algorithm is needed to optimally choose a trajectory through the sky that a sensor should take to search a set of orbits. Because of the malleable definition of a set, this allows EOS to be tasked on a wide variety of search and reacquisition problems. Next, when taking actual data the sensor exposure time, slew rate, and campaign length need to be chosen to optimize the quality of image data. These tasking parameters are chosen with respect to the detection and estimation algorithms themselves, which all relate back to a maximum likelihood method. Next, the object detection algorithms should be as sensitive as possible. This enables a larger network of lower cost telescopes to be deployed, and ensures that performance is robust to light pollution, enabling new telescope locations. These types of networks are needed to allow the kinds of sensor architectures which support interesting handoff and reacquisition problems. Finally, to make proper telescope communication, hand-off, and long term reacquisition possible, detection algorithms should utilize any prior information (search set) on a particular object or class of objects for more sensitive and efficient detection. This supports hand-off between arbitrary locations and longer delay times before reacquisition from the detection side of the problem.