Volume 40 Issue 4
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Article Navigation >  Journal of Space Science  > 2020  >  40(4): 554-561
MA Yuanye, ZHENG Jianhua, LI Mingtao. Optimal Design of Revisit Constellation Based on Coverage Analysis of Low Density Grids Model[J]. Journal of Space Science, 2020, 40(4): 554-561. doi: 10.11728/cjss2020.04.554
Citation: MA Yuanye, ZHENG Jianhua, LI Mingtao. Optimal Design of Revisit Constellation Based on Coverage Analysis of Low Density Grids Model[J]. Journal of Space Science, 2020, 40(4): 554-561. doi: 10.11728/cjss2020.04.554
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Optimal Design of Revisit Constellation Based on Coverage Analysis of Low Density Grids Model

doi: 10.11728/cjss2020.04.554

  • 1 National Space Science Center, Chinese Academy of Sciences, Beijing 101408;

  • 2 University of Chinese Academy of Sciences, Beijing 101407

  • Received Date: 2019-02-14
  • Rev Recd Date: 2019-11-20
  • Publish Date: 2020-07-15
    Abstract
  • Fast revisit constellation can quickly obtain the state information of local or global targets, which has the advantages of continuity, and is widely used in the fields of remote sensing, network communication, meteorological observation, near Earth space environment detection. Therefore, it is of great significance to study the optimal design of the fast revisit constellation. Grid point analysis is the most commonly used method in constellation design, but its calculation is large. In this paper a constellation optimization design method based on low-density grid coverage analysis for revisiting constellation optimization design is proposed, which combines the regular polyhedron spherical partition model and the sampling point grouping scheme. This method reduces the computation by more than 80% under the premise of ensuring the accuracy of the design results. Experiments show that the multi-group constellation configuration scheme optimized by this method has good revisit characteristics, and greatly reduces the optimization design time. Moreover, compared with the statistical results of using high-density grid, the error of the optimized design results is very small.

     

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