Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Artificial Intelligence Approaches for GPS GDOP Classification

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 16
Year of Publication: 2014
Nadali Zarei

Nadali Zarei. Article: Artificial Intelligence Approaches for GPS GDOP Classification. International Journal of Computer Applications 96(16):16-21, June 2014. Full text available. BibTeX

	author = {Nadali Zarei},
	title = {Article: Artificial Intelligence Approaches for GPS GDOP Classification},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {16},
	pages = {16-21},
	month = {June},
	note = {Full text available}


Geometrical dilution of precision (GDOP) concept is a powerful and widespread quantify for determining the errors resulting from satellite configuration geometry. GDOP computation is based on the complicated transformation and inversion of measurement matrices that has a time and power burden. Also, basic back propagation neural network (BPNN) is easy to fall into local minima. To overcome this problem, in this study we propose an approach based on neural network (NN) and evolutionary algorithms (EAs) for GPS GDOP classification. In this article we use a number of EAs such as genetic algorithm (GA), particle swarm optimization (PSO), new PSO (NPSO), and imperialist competitive algorithm (ICA) to train an NN. Simulation results illustrate that the proposed methods have superiority performance.


  • D. J. Jwo and C. C. Lai, "Neural network-based GPS GDOP approximation and classification", Journal of GPS Solutions, vol. 11, no. 1, pp. 51-60, 2007.
  • M. Zhang and J. Zhang, "A fast satellite selection algorithm: beyond four satellites", IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 5, pp. 740-747, 2009.
  • R. Yarlagadda, I. Ali, N. Al-Dhahir and J. Hershey, "GPS GDOP metric", IEE Proc. -Radar, Sonar Navig, vol. 147, no. 5, pp. 259-264, 2000.
  • H. Azami, S. Sanei and H. Alizadeh, "GPS GDOP Classification via Advanced Neural Network Training" International Conference on Contemporary Issues in Computer and Information Sciences, Brown Walker press, USA, pp. 315-320, 2012.
  • H. Azami, M. R. Mosavi and S. Sanei, "Classification of GPS satellites using improved back propagation training algorithms", Wireless Personal Communications, vol. 71, no. 2, pp. 789-803, 2013.
  • D. Simon and H. El-Sherief, "Navigation satellite selection using neural networks", Journal of Neurocomputing, vol. 7, no. 3, pp. 247–258, 1995.
  • H. Azami and S. Sanei, "GPS GDOP classification via improved neural network trainings and principal component analysis", International Journal of Electronics, Taylor & Francis, http://dx. doi. org/10. 1080/00207217. 2013. 832390, pp. 1-14, 2013.
  • M. R. Mosavi and H. Azami, "Applying neural network ensembles for clustering of GPS satellites", Journal of Geoinformatics, vol. 7, no. 3, pp. 7-14, 2011.
  • M. R. Mosavi and M. Shiroie, "Efficient evolutionary algorithms for GPS satellites classification", Arabian Journal for Science and Engineering, vol. 37, no. 7, pp. 2003-2015, 2012.
  • L. Mussi, S. Cagnoni, and F. Daolio, "GPU-based road sign detection using particle swarm optimization", International Conference on Intelligent Systems Design and applications, pp. 152-157, 2009.
  • H. Azami, M. Malekzadeh and S. Sanei, "Optimization of orthogonal polyphase Coding waveform for MIMO radar based on evolutionary algorithms", Journal of mathematics and Computer Science, vol. 6, pp. 146-153, 2013.
  • C. Yang and D. Simon, "A new particle swarm optimization technique", International Conference on Systems Engineering, pp. 164-169, 2005.
  • H. Azami, S. Sanei, K. Mohammadi and H. Hassanpour, "A hybrid evolutionary approach to segmentation of non-stationary signals", Digital Signal Processing, vol. 23, no. 4, pp. 1103-1114, 2013.
  • E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition", IEEE Congress on Evolutionary Computation, pp. 4661-4666, 2007.
  • M. Abdechiri, K. Faez and H. Bahrami, "Neural network learning based on chaotic imperialist competitive algorithm", 2nd International Intelligent Systems and Applications (ISA), pp. 1-5, 2010.
  • E. Atashpaz-Gargari, F. Hashemzadeh, R. Rajabioun and C. Lucas, "Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process", International Journal of Intelligent Computing and Cybernetics, vol. 1, no. 3, pp. 337-355, 2008.
  • R. Rajabioun, E. Atashpaz-Gargari and C. Lucas, "Colonial competitive algorithm as a tool for Nash equilibrium point achievement", Lecture Notes in Computer Science, pp. 680-695, 2008.
  • E. Atashpaz-Gargari, R. Rajabioun, F. Hashemzadeh and F. Salmasi, "A decentralized PID controller based on optimal shrinkage of gershgorin bands and PID tuning using colonial competitive algorithm", International Journal of Innovative Computing, Information and Control (IJICIC), vol. 5, no. 10, pp. 3227-3240, 2009.