Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach
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 an...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/1235692 |
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| _version_ | 1832555539116064768 |
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| author | Tania Savitri Youngjoo Kim Sujang Jo Hyochoong Bang |
| author_facet | Tania Savitri Youngjoo Kim Sujang Jo Hyochoong Bang |
| author_sort | Tania Savitri |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6ca9befd84b94a72a62d060ed9d5c43b |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-6ca9befd84b94a72a62d060ed9d5c43b2025-02-03T05:47:58ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742017-01-01201710.1155/2017/12356921235692Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical ApproachTania Savitri0Youngjoo Kim1Sujang Jo2Hyochoong Bang3Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of KoreaDepartment of Aerospace Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of KoreaKorea Aerospace Research Institute (KARI), Daejeon 305-701, Republic of KoreaDepartment of Aerospace Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of KoreaThis 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.http://dx.doi.org/10.1155/2017/1235692 |
| spellingShingle | Tania Savitri Youngjoo Kim Sujang Jo Hyochoong Bang Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach International Journal of Aerospace Engineering |
| title | Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach |
| title_full | Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach |
| title_fullStr | Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach |
| title_full_unstemmed | Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach |
| title_short | Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach |
| title_sort | satellite constellation orbit design optimization with combined genetic algorithm and semianalytical approach |
| url | http://dx.doi.org/10.1155/2017/1235692 |
| work_keys_str_mv | AT taniasavitri satelliteconstellationorbitdesignoptimizationwithcombinedgeneticalgorithmandsemianalyticalapproach AT youngjookim satelliteconstellationorbitdesignoptimizationwithcombinedgeneticalgorithmandsemianalyticalapproach AT sujangjo satelliteconstellationorbitdesignoptimizationwithcombinedgeneticalgorithmandsemianalyticalapproach AT hyochoongbang satelliteconstellationorbitdesignoptimizationwithcombinedgeneticalgorithmandsemianalyticalapproach |