Skip to main content
Log in

Optimization of earth observation satellite system based on parallel systems and computational experiments

  • Published:
Journal of Control Theory and Applications Aims and scope Submit manuscript

Abstract

Earth observation satellite system (EOSS) is the main space platform to collect ground information. Optimization of EOSS is still a difficult problem, as it is a complex system concerning a great deal of design variables and uncertain factors. To solve the problem, an optimization framework based on parallel system and computational experiments is proposed. An artificial system for EOSS is firstly constructed, which is the integration of resource data, task data, environment data and related operation rules. Real EOSS together with artificial EOSS constitute the parallel systems for EOSS. Based on the parallel systems, concept of computational experiments is detailed. Moreover, surrogate models are built to approximate real EOSS. Genetic algorithm and improved general pattern search method are adopted to optimize the model. According to the framework, a case study is carried out. Through the results, we illustrated the proposed framework to be useful and effective for EOSS optimization problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. T. Mosher. Spacecraft design using a genetic algorithm optimization approach. Proceedings of the IEEE Aerospace Conference. Aspen: IEEE, 1998: 123–134.

    Google Scholar 

  2. G. B. Shaw. The Generalized Information Network Analysis Methodology for Distributed Satellite Systems. Cambridge: Massachusetts Institute of Technology, 1998.

    Google Scholar 

  3. C. D. Jilla. A Multiobjective, Multidisciplinary Design Optimization Methodology for the Conceptual Design of Distributed Satellite Systems. Cambridge: Massachusetts Institute of Technology, 2002.

    Google Scholar 

  4. A. M. Ross. Multi-attribute Tradespace Exploration with Concurrent Design as A Value-centric Framework for Space System Architecture and Design. Cambridge: Massachusetts Institute of Technology, 2003.

    Google Scholar 

  5. N. P. Diller. Utilizing Multiple Attribute Tradespace Exploration with Concurrent Design for creating Aerospace Systems Requirements. Cambridge: Massachusetts Institute of Technology, 2002.

    Google Scholar 

  6. M. G. Richards. Multi-attribute Tradespace Exploration for Survivability. Cambridge: Massachusetts Institute of Technology, 2009.

    Google Scholar 

  7. C. Rivett, C. Pontecorvo. Improving Satellite Surveillance through Optimal Assignment of Assets. Australia: DSTO Information Sciences Laboratory, 2003.

    Google Scholar 

  8. F. Wang. Toward a paradigm shift in social computing: the acp approach. IEEE Intelligent Systems, 2007, 22(5): 65–67.

    Article  Google Scholar 

  9. F. Wang, S. Tang. Artificial societies for integrated and sustainable development of metropolitan systems. IEEE Intelligent Systems, 2004, 19(4): 82–87.

    Article  Google Scholar 

  10. F. Wang, K. M. Carley, D. Zeng, et al. Social computing: from social informatics to social intelligence. IEEE Intelligent Systems, 2007, 22(2): 79–83.

    Article  Google Scholar 

  11. X. Liu, Y. Chen, X. Jing, et al. Comprehensive latin hypercube sam- pling method and application. Conference of Modern Manufacture and Integration Technology. Beijing: Computer Integrated Manufacturing Systems, 2010: 175–182.

    Google Scholar 

  12. I. J. Forrester, A. J. Keane. Recent advances in surrogate-based optimization. Progress in Aerospace Sciences. 2009, 45(1/3): 50–79.

    Article  Google Scholar 

  13. A. K. E. Joshua. Extending Orthogonal and Nealy Orthogonal Latin Hypercube Designs for Complex Simulation and Experimentation. Monterey: Naval Postgraduate School, 2006.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaolu Liu.

Additional information

This work was supported by the National Natural Science Foundation of China (Nos. 71071156, 70971131).

Xiaolu LIU is a lecturer at the School of Information System and Management, National University of Defense Technology. Her research interests cover scheduling of imaging satellites and optimization of earth observation satellite system.

Yingguo CHEN is a Ph.D. candidate at the National University of Defense Technology. He received his B.S. degree from National University of Defense Technology in 2008. His research interests cover optimization of earth observation satellite system.

Renjie HE is an associate professor at the School of Information System and Management, National University of Defense Technology. His research interests cover technology of system management and comprehensive integration.

Yingwu CHEN is a professor at the School of Information System and Management, National University of Defense Technology. His research interests cover system planning and management decision-making, SoS engineering management.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, X., Chen, Y., Chen, Y. et al. Optimization of earth observation satellite system based on parallel systems and computational experiments. J. Control Theory Appl. 11, 200–206 (2013). https://doi.org/10.1007/s11768-013-2033-y

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11768-013-2033-y

Keywords

Navigation