Multi-Objective CubeSat Constellation Optimization for Space Situational Awareness

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SnowA-8900.pdf (4.45 MB)
Author(s)
Snow, Adam C.
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Holzinger, Marcus J.
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Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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https://hdl.handle.net/1853/73534
Abstract
The proliferation of on-orbit debris has motivated much of the recent space situ ational awareness (SSA) missions and related research. Space-based missions are typically carried out by large spacecraft, yet the emerging and improving technol ogy for CubeSat class satellites offers a potential new platform for SSA. This paper presents the graduate Special Problem effort to develop explore the optimization of a CubeSat constellation for SSA. This optimization approach considers two ob jectives: to maximize the number of daily unique detections while minimizing the lifecycle cost of a constellation. The epsilon constraint method is used to devel op the Pareto Frontier with a genetic algorithm as the single-objective optimizer. This work was prepared as part of a larger effort for the Journal of Spacecraft and Rockets, and the supporting material is included.
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Date
2015-12-01
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Masters Project
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