[1. Azami, H., Malekzadeh, M. and Sanei, S. (2013) Optimization of orthogonal polyphase coding waveform for MIMO radar based on evolutionary algorithms, Journal of mathematics and Computer Science, Vol. 6, pp. 146-153.]Search in Google Scholar
[2. Briers, M., Doucet, A. and Maskell, S. (2010) Smoothing algorithms for state-space models. Annals of the Institute of Statistical Mathematics, Springer, pp.61-89.10.1007/s10463-009-0236-2]Search in Google Scholar
[3. Cui, X.H. and Polok, T.E. (2005) Document clustering using particle swarm optimization. In: Proceedings of swarm intelligence symposium, IEEE, pp. 185-191, Los Alamitos.10.1109/SIS.2005.1501621]Search in Google Scholar
[4. Daehee Won, Precise Positioning, http://smileforday.com/?page_id=84]Search in Google Scholar
[5. Dehuri, S. and Tripathy, S. (2011) An extended bayesian/HAPSO intelligent method in intrusion detection system. Knowledge Mining Using Intelligent Agents, Vol. 6, pp. 133.]Search in Google Scholar
[6. GPSTk, The GPS Toolkit, http://www.gpstk.org.]Search in Google Scholar
[7. IGS Data, http://sopac.ucsd.edu/dataBrowser.html.]Search in Google Scholar
[8. Jgouta, M. and Nsiri, B. (2015) Statistical estimation of GNSS pseudo-range errors, Procedia Computer Science, Elsevier, vol. 73, pp. 258-265.]Search in Google Scholar
[9. Jwo, D.J. and Weng, T.P. (2008) An adaptive sensor fusion method with applications in integrated navigation, Journal of Navigation, Vol. 61, No. 4, pp.705-721.]Search in Google Scholar
[10. Leonard, J.A. and Kramer, M.A. (1991) Radial basis function networks for classifying process faults, Control Systems, IEEE, Vol. 11, No. 3, pp. 31-38.]Search in Google Scholar
[11. Li, J. and Li, B. (2014) Parameters selection for support vector machine based on particle swarm optimization. In Intelligent Computing Theory, Springer International Publishing, pp. 41-47.10.1007/978-3-319-09333-8_5]Search in Google Scholar
[12. Mosavi, MR. and Rahemi, N. (2015) Positioning performance analysis using RWLS algorithm based on variance estimation methods, Aerospace Science and Technology, pp.88-96.]Search in Google Scholar
[13. Mussi, L., Cagnoni, S. and Daolio, F. (2009) GPU-based road sign detection using particle swarm optimization, International Conference on Intelligent Systems Design and applications, pp. 152-157.]Search in Google Scholar
[14. NGA, National Geospatial-Intelligence Agency, http://eartch-info.nga.mil/GandG/Sathtml/.]Search in Google Scholar
[15. Noureldin, A., Osman, A. and Elsheimy, N. (2004) A neuro-wavelet method for multi-sensor system integration for vehicular navigation, Measurement Science and Technology, Vol. 15, No. 2, pp. 404-412.]Search in Google Scholar
[16. Santerre, R. Roy, E. and Parrot, D. (1995) Positionnement GPS avec des measures de pseudo distance filtrées et lissées, Lighthouse-Burlington, pp. 21-30.]Search in Google Scholar
[17. Shen, C. Cao, G.Y and Zhu, X.J. (2002) Nonlinear modelling of MCFC stack based on RBF neural networks identification, Simulation Modelling Practice and Theory, Vol.10, No.1, pp.109-119.]Search in Google Scholar
[18. Sun, T-Y., Liu, C-C., Lin, C-L., Hsieh, S-T. and Huang, C-S. (2009) A radial basis function neural network with adaptive structure via particle swarm optimization. - http://www.intechopen.com/books/particle_swarm_optimization/a_radial_basis_function_neural_network_with_adaptive_structure_via_particle_swarm_optimization.10.5772/6763]Search in Google Scholar