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http://dx.doi.org/10.5139/JKSAS.2008.36.7.666

Optimal Scheduling of Satellite Tracking Antenna of GNSS System  

Ahn, Chae-Ik (서울대학교 기계항공공학부 대학원)
Shin, Ho-Hyun (서울대학교 기계항공공학부 대학원)
Kim, You-Dan (서울대학교 기계항공공학부)
Jung, Seong-Kyun (한국전자통신연구원)
Lee, Sang-Uk (한국전자통신연구원)
Kim, Jae-Hoon (한국전자통신연구원)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.36, no.7, 2008 , pp. 666-673 More about this Journal
Abstract
To construct the accurate radio satellite navigation system, the efficient communication each satellite with the ground station is very important. Throughout the communication, the orbit of each satellite can be corrected, and those information will be used to analyze the satellite satus by the operator. Since there are limited resources of ground station, the schedule of antenna's azimuth and elevation angle should be optimized. On the other hand, the satellite in the medium earth orbit does not pass the same point of the earth surface due to the rotation of the earth. Therefore, the antenna pass schedule must be updated at the proper moment. In this study, Q learning approach which is a form of model-free reinforcement learning and genetic algorithm are considered to find the optimal antenna schedule. To verify the optimality of the solution, numerical simulations are conducted.
Keywords
Antenna optimal schedule; TSP; Traveling Salesman Problem; Q Learning; Genetic Algorithm;
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  • Reference
1 J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975
2 R. Haupt, and S. Haupt, Practical Genetic Algorithms, Wiley & Sons, Inc., Hoboken, New Jersey, 2004
3 배상현, 정현철, Neural Networks와 유전자 알고리즘, 조선대학교 출판부, 광주, 2004
4 J. Stender, E. Hillebrand, and J. Kingdon, Genetic Algorithms in Optimizatin, Simulation, and Modelling, IOS Press, Amsterdam, 1994
5 J. Grefentette, R. Gopal, B. Rosmaita, and D. Van Gucht, "Genetic Algorithm for the TSP", Proceedings of the 1st International Conference on Genetic Algorithms, Hillsdale, NJ, pp. 160-168, 1985
6 L. Wang, J. Zhang, H. Li, "An Improved Genetic Algorithm for TSP", Proceedings of the 6th International Conference on Machine Learning and Cybernetics, Hong Kong, Aug., 2007
7 J. Hopfield, and D. Tank, "Neural Computation of Decisions in Optimization Problems", Biological Cybernetics, Springer-Verlag, Vol. 52, pp. 141-152, 1985
8 T. Nakaguchi, K. Jin'no, and M. Tanaka, "Hysteresis Neural Networks for Solving Traveling Salesperson Problems", IEEE International Symposium on Circuits and Systems, Geneva, Switzerland, May. 2000
9 R. Sutton, and A. Barto, Reinforcement Learning: An Introduction, The MIT Press, Cambridge, Massachusetts, 1998
10 J. Jang, C. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall, Upper Saddle River, New Jersey, 1997
11 T. Mitchell, Machine Learning, McGRAW-HILL, Singapore, 1997
12 G. Reinelt, The Traveling Salesman : Computational Solutions for TSP Applications, Springer-Verlag, New York, 1994