Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach

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This paper focuses on maximizing the percent coverage and minimizing the revisit time for a small satellite constellation with limited coverage. A target area represented by a polygon defined by grid points is chosen instead of using a target point only. The constellation consists of nonsymmetric and circular Low Earth Orbit (LEO) satellites. A global optimization method, Genetic Algorithm (GA), is chosen due to its ability to locate a global optimum solution for nonlinear multiobjective problems. From six orbital elements, five elements (semimajor axis, inclination, argument of perigee, longitude of ascending node, and mean anomaly) are varied as optimization design variables. A multiobjective optimization study is conducted in this study with percent coverage and revisit time as the two main parameters to analyze the performance of the constellation. Some efforts are made to improve the objective function and to minimize the computational load. A semianalytical approach is implemented to speed up the guessing of initial orbital elements. To determine the best parametric operator combinations, the fitness value and the computational time from each study cases are compared.
Publisher
HINDAWI LTD
Issue Date
2017
Language
English
Article Type
Article
Keywords

COVERAGE

Citation

INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING

ISSN
1687-5966
DOI
10.1155/2017/1235692
URI
http://hdl.handle.net/10203/224752
Appears in Collection
AE-Journal Papers(저널논문)
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