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Payan, Alexia P.

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    Framework for Multi-Asset Comparison and Rapid Down-selection for Earth Observation Missions
    (Georgia Institute of Technology, 2019) Gilleron, Jerome ; Muehlberg, Marc ; Payan, Alexia P. ; Choi, Youngjun ; Briceno, Simon ; Mavris, Dimitri N.
    Observing the Earth, whether it be from space or from the air, has become easier in recent years with the advent of new space-borne and airborne technologies. First, satellites constantly provide data about almost any point on the globe with varying resolutions and in various spectral bands. Second,Unmanned Aerial Vehicles (UAV) are becoming more readily accessible to the public and may be rapidly deployed to take high resolution images of ground features or areas of interest. Third, manned aircraft may be used to image large areas of land at a higher resolution than satellites and have been used regularly in disaster monitoring and surveillance missions. However, when multiple heterogeneous assets compete to perform a given aerial imaging mission, deciding which asset is better suited and/or less costly to operate in a timely manner is challenging. Every acquisition mode is different, resolution values are computed differently and there currently does not exist a common framework to compare UAV, manned aircraft and satellites. To address this challenge, this paper describes a methodology to rapidly compare various types of aerial assets (such as UAVs and manned aircraft) and space assets (such as satellites) to decide which one would be better able to perform an Earth observation mission depending on a set of requirements. To demonstrate the proposed methodology, this paper executes numerical simulations with three different representative scenarii in California.
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    Uncovering local magnetospheric processes governing the morphology and periodicity of Ganymede’s aurora using three-dimensional multifluid simulations of Ganymede’s magnetosphere
    (Georgia Institute of Technology, 2013-04-08) Payan, Alexia P.
    The electrodynamic interaction of Ganymede’s mini-magnetosphere with Jupiter’s corotating magnetospheric plasma has been shown to give rise to strong current systems closing through the moon and its ionosphere as well as through its magnetopause and magnetotail current sheet. This interaction is strongly evidenced by the presence of aurorae at Ganymede and of a bright Ganymede footprint on Jupiter’s ionosphere. This footprint is located equatorward of the main auroral emissions, at the magnetic longitude of the field line threading Ganymede. The brightness of Ganymede’s auroral footprint at Jupiter along with its latitudinal position have been shown to depend on the position of Ganymede relative to the center of the Jovian plasma sheet. Additionally, observations using the Hubble Space Telescope showed that Ganymede’s auroral footprint brightness is characterized by variations of three different timescales: 5 hours, 10-40 minutes, and ~100 seconds. The goal of the present study is to examine the relationship between the longest and the shortest timescale periodicities of Ganymede’s auroral footprint brightness and the local processes occurring at Ganymede. This is done by coupling a specifically developed brightness model to a three-dimensional multifluid model which tracks the energies and fluxes of the various sources of charged particles that precipitate into Ganymede’s ionosphere to generate the aurora. It is shown that the predicted auroral brightnesses and morphologies agree well with observations of Ganymede’s aurora from the Hubble Space Telescope. Our results also suggest the presence of short- and long-period variabilities in the auroral emissions at Ganymede due to magnetic reconnections on the magnetopause and in the magnetotail, and support the hypothesis of a correlation between the variability of Ganymede’s auroral footprint on Jupiter’s ionosphere and the variability in the brightness and morphology of the aurora at Ganymede. Finally, the modeled aurora at Ganymede reveals that the periodicities in the morphology and brightness of the auroral emissions are produced by two different dynamic reconnection mechanisms. The Jovian flow facing side aurora is generated by electrons sourced in the Jovian plasma and penetrating into Ganymede’s ionosphere through the cusps above the separatrix region. In this case, the reconnection processes responsible for the auroral emissions occur on Ganymede’s magnetopause between the Jovian magnetic field lines and the open magnetic field lines threading Ganymede’s Polar Regions. As for the magnetotail side aurora, it is generated by electrons originating from Ganymede’s magnetospheric flow. These electrons are accelerated along closed magnetic field lines created by magnetic reconnection in Ganymede’s magnetotail, and precipitate into Ganymede’s ionosphere at much lower latitudes, below the separatrix region.
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    Enabling methods for the design and optimization of detection architectures
    (Georgia Institute of Technology, 2013-04-08) Payan, Alexia P.
    The surveillance of geographic borders and critical infrastructures using limited sensor capability has always been a challenging task in many homeland security applications. While geographic borders may be very long and may go through isolated areas, critical assets may be large and numerous and may be located in highly populated areas. As a result, it is virtually impossible to secure each and every mile of border around the country, and each and every critical infrastructure inside the country. Most often, a compromise must be made between the percentage of border or critical asset covered by surveillance systems and the induced cost. Although threats to homeland security can be conceived to take place in many forms, those regarding illegal penetration of the air, land, and maritime domains under the cover of day-to-day activities have been identified to be of particular interest. For instance, the proliferation of drug smuggling, illegal immigration, international organized crime, resource exploitation, and more recently, modern piracy, require the strengthening of land border and maritime awareness and increasingly complex and challenging national security environments. The complexity and challenges associated to the above mission and to the protection of the homeland may explain why a methodology enabling the design and optimization of distributed detection systems architectures, able to provide accurate scanning of the air, land, and maritime domains, in a specific geographic and climatic environment, is a capital concern for the defense and protection community. This thesis proposes a methodology aimed at addressing the aforementioned gaps and challenges. The methodology particularly reformulates the problem in clear terms so as to facilitate the subsequent modeling and simulation of potential operational scenarios. The needs and challenges involved in the proposed study are investigated and a detailed description of a multidisciplinary strategy for the design and optimization of detection architectures in terms of detection performance and cost is provided. This implies the creation of a framework for the modeling and simulation of notional scenarios, as well as the development of improved methods for accurate optimization of detection architectures. More precisely, the present thesis describes a new approach to determining detection architectures able to provide effective coverage of a given geographical environment at a minimum cost, by optimizing the appropriate number, types, and locations of surveillance and detection systems. The objective of the optimization is twofold. First, given the topography of the terrain under study, several promising locations are determined for each sensor system based on the percentage of terrain it is covering. Second, architectures of sensor systems able to effectively cover large percentages of the terrain at minimal costs are determined by optimizing the number, types and locations of each detection system in the architecture. To do so, a modified Genetic Algorithm and a modified Particle Swarm Optimization are investigated and their ability to provide consistent results is compared. Ultimately, the modified Particle Swarm Optimization algorithm is used to obtain a Pareto frontier of detection architectures able to satisfy varying customer preferences on coverage performance and related cost.