Average and Maximum Revisit Time Trade Studies for Satellite Constellations Using a Multiobjective Genetic Algorithm
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Average and Maximum Revisit Time Trade Studies for Satellite Constellations Using a Multiobjective Genetic Algorithm Edwin A. Williams,ยท William A. Crossley/ and Thomas J. Lang:' Abstract Recently, versions of the Genetic Algorithm (GA) have successfully generated low-Earth orbit sparse coverage satellite constellations that appear to outperform traditionally developed constellations. The objective of these constellations was to minimize the maximum revisit time over a latitude band of interest. However, many constellation designers are also concerned with the average revisit time, and contrary to expectations, these two objectives often compete with each other. This paper presents a multiobjective GA approach to generate numerous constellation designs that show the trade-off between the revisit time objectives. These trade studies are conducted using a single run of the multiobjective GA. The designs generated using this approach are discussed and some trends are examined.
Introduction Revisit time is a metric used to evaluate satellite constellations that do not achieve continuous coverage of an area on the Earth's surface; these constellations are often called sparse coverage constellations. Revisit time is the time for which a given location on the Earth is not viewable by a satellite. For applications where sparse coverage is acceptable, it seems natural to design a constellation so that the longest time gap in coverage for any point on the Earth in the desired zone of latitudes is minimized. This is the approach pursued in most sparse coverage constellation design problems. The sparse coverage constellation design problem can be posed as an optimization problem in which the objective is to minimize the maximum revisit time and the 'Graduate Student, School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907-1282; currently Advanced Engine Performance Engineer, Pratt & Whitney, East Hartford, CT 06108. 2 Associate Professor, School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907-1282. 3Director, Astrodynamics Department, The Aerospace Corporation, El Segundo, CA 90245-4691. 385
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design variables are the constellation parameters (e.g. inclination, satellite right ascension, and mean anomaly). Researchers have successfully employed global optimization methods to generate satellite constellation designs for the single objective of maximum revisit time [1,2]. Using this objective, the optimization routine concentrates on the coverage gaps that are the largest in duration and does not consider the average value of the revisit time. For this reason, it is not uncommon to find that by minimizing the maximum revisit time, the average revisit time has actually increased. Conversely, designing a constellation to minimize the average revisit time will reduce the revisit time for most points, but this may allow some locations to have large revisit times. Therefore, these two objectives (average and maximum revisit time) often compet
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