Arabian Journal for Science and Engineering

, Volume 37, Issue 7, pp 2003–2015 | Cite as

Efficient Evolutionary Algorithms for GPS Satellites Classification

Research Article - Electrical Engineering

Abstract

In this paper, the classification methods based on evolutionary algorithms (EAs) for choosing optimum satellites subset in GPS receivers are proposed. These methods use EAs to achieve best optimum answers. Four EAs including simulating annealing, genetic algorithm, particle swarm optimization and bee algorithm are used and compared with each other. These methods try to find the subset which has the lowest geometric dilution of precision (GDOP). The GDOP is a measure of the overall uncertainty in a GPS position solution. The most important advantage of these methods is that there is no need to calculate inverse matrix; therefore, they reduce computational burden. Also, in contrast with classification methods based on neural networks, these algorithms do not need training process, so they are very fast. The proposed methods are simulated and validated by a software simulation. The simulation results demonstrate that bee algorithm has greater accuracy and calculation time than other methods, so that it can improve classification accuracy of GPS satellites about 99.86 %.

Keywords

GPS GDOP Classification Simulating annealing Genetic algorithm Particle swarm optimization Bee algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yarlagadda R., Ali I., Al-Dhahir N., Hershey J.: GPS GDOP Metric. IEEE Proc. Radar Sonar Navig. 147, 259–264 (2000)CrossRefGoogle Scholar
  2. 2.
    Zhang M., Zhang J.: A Fast Satellite selection algorithm: beyond four satellites. IEEE J. Sel. Top. Signal Process. 3, 740–747 (2009)CrossRefGoogle Scholar
  3. 3.
    Jwo D.J., Lai C.C.: Neural network-based GPS GDOP approximation and classification. J. GPS Solut. 11, 51–60 (2007)CrossRefGoogle Scholar
  4. 4.
    Simon D., El-Sherief H.: Navigation satellite selection using neural networks. J. Neurocomput. 7, 247–258 (1995)MATHCrossRefGoogle Scholar
  5. 5.
    Doong S.H.: A closed-form formula for GPS GDOP computation. J. GPS Solut. 13, 183–190 (2009)CrossRefGoogle Scholar
  6. 6.
    Wu, C.H., Su, W.H.: A comparative study on regression models of GPS GDOP using soft-computing techniques. IEEE Conf. Fuzzy Syst. 1513–1516 (2009)Google Scholar
  7. 7.
    Zirari, S., Canalda, P., Spies, F.: A very first geometric dilution of precision proposal for wireless access mobile networks. IEEE Conf. Adv. Satell. Space Commun. 162–167 (2009)Google Scholar
  8. 8.
    Wu C.H., Su W.H. Ho, Su W.H. Ho: A study on GPS GDOP approximation using support-vector machines. IEEE Trans. Instrum. Meas 60, 137–145 (2011)CrossRefGoogle Scholar
  9. 9.
    Xu R., Wunsch D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16, 645–678 (2005)CrossRefGoogle Scholar
  10. 10.
    Kekec, N., Yumusak, N., Celebi, N.: Data mining and clustering with ant colony optimization. In: 5th Symposium on Intelligent Manufacturing Systems, pp. 1178–1190 (2006)Google Scholar
  11. 11.
    Dorigo M., Blum C.: Ant colony optimization theory: a survey. J. Theor. Comput. Sci. 344, 243–278 (2005)MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Bryan K., Cunningham P., Bolshakova N.: Application of simulated annealing to the biclustering of gene expression data. IEEE Trans. Inf. Technol. Biomed. 10, 519–525 (2006)CrossRefGoogle Scholar
  13. 13.
    Srinivas M., Patnaik L.M.: Genetic algorithms: a survey. IEEE Trans. Comput. 27, 17–26 (1994)Google Scholar
  14. 14.
    Kennedy J., Eberhart R.: Particle swarm optimization. IEEE Conf. Neural Netw. 4, 1942–1948 (1995)Google Scholar
  15. 15.
    Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. IEEE Conf. Evol. Comput. 69–73 (1998)Google Scholar
  16. 16.
    Duran, O., Rodriguez, N., Consalter, L.A.: A PSO-based clustering algorithm for manufacturing cell design. In: IEEE Workshop on Knowledge Discovery and Data Mining, pp. 72–75 (2008)Google Scholar
  17. 17.
    Higashi, N., Iba, H.: Particle swarm optimization with gaussian mutation. In: IEEE Symposium on Swarm Intelligence, pp. 72–79 (2003)Google Scholar
  18. 18.
    Mala D.J., Mohan V., Kamalapriya M.: Automated software test optimization framework—an artificial bee colony—optimization-based approach. J. IET Softw. 4, 334–348 (2010)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum and Minerals 2012

Authors and Affiliations

  1. 1.Department of Electrical EngineeringIran University of Science and TechnologyTehranIran

Personalised recommendations