Real-Time Differential Global Poisoning System Stability and Accuracy Improvement by Utilizing Support Vector Machine

  • Mohammad Hossein Refan
  • Adel Dameshghi
  • Mehrnoosh Kamarzarrin


Due to errors, accuracy of Global Positioning System is not so high. Therefore, the Real Time Differential Global Poisoning System (RTDGPS) which is based on the successive transmission message of RTCM protocol, is using in real-time applications. Stability and accuracy of the system, significantly depends to a fast transmission of correction messages. These messages come from the reference station to the user stations and affected by the errors related to each satellite. Receiving correction factors which are transmitted by the reference station are facing with time-lag problem, which can increase the error of the RTDGPS. To overcome this problem, prediction algorithms are used. In this research, support vector machine (SVM) model is used to predict the pseudo range correction. Unfortunately, the practical use of SVM is limited because the quality of SVM models depends on a proper setting of SVM and SVM kernel parameters. Therefore, to determine the main parameters of the SVM, both particle swarm optimization (PSO) and genetic algorithm (GA), as two optimization techniques, are used. The proposed methodology has been implemented by a 6-s predicts time step. Simulations showed that the accuracy of GA–SVM and PSO–SVM are equal to 0.186 and 0.154, respectively.


Real Time Differential Global Position System GA–SVM PRC PSO–SVM 



Artificial neural network


Autoregressive moving average


Empirical risk minimization


Genetic algorithm


Global positioning system


Grid search


Model complexity minimization


National Marine Electronics Association




Pseudo range correction


Particle swarm optimization


Radial basis function


Range rate correction


Radio Technical Commission for Maritime Services


Real Time Differential Global Poisoning System


Support vector machine


Support vector regression


Transistor–transistor logic


  1. 1.
    P. K. Enge, The Global Positioning System: Signals, measurements, and performance, International Journal of Wireless Information Networks, Vol. 1, No. 2, pp. 83–105, 1994.CrossRefGoogle Scholar
  2. 2.
    Sh. Chuang, Y. Wenting, S. Weiwei, L. Yidong, Y. yibin, Z. Rui, GLONASS pseudorange inter-channel biases and their effects on combined GPS/GLONASS precise point positioning, GPS Solutions, Vo. 17, No. 4, pp. 439-451, 2013.Google Scholar
  3. 3.
    H. Bock, R. Dach, Y. Yoon and O. Montenbruck, GPS clock correction estimation for near real-time orbit determination Applications, Aerospace Science and Technology, Vol. 13, No. 7, pp. 415–422, 2009.CrossRefGoogle Scholar
  4. 4.
    M. Mohasseb, A. Rabbany, O. Alim and R. Rashad, DGPS correction prediction using artificial neural networks, The Journal of Navigation, Vol. 60, No. 2, pp. 291–301, 2007.CrossRefGoogle Scholar
  5. 5.
    J. Zhang, K. Zhang, R. Grenfell and R. Deakin, GPS satellite velocity and acceleration determination using the broadcast ephemeris, The Journal of Navigation, Vol. 59, No. 2, pp. 293–305, 2006.CrossRefGoogle Scholar
  6. 6.
    M. R. Mosavi, Comparing DGPS Corrections Prediction using Neural Network, Fuzzy Neural Network, and Kalman Filter, GPS Solutions, Vol. 10, No. 2, pp. 97–107, 2006.CrossRefGoogle Scholar
  7. 7.
    Y. Zhang and Ch G Bartone, A real-time meteorological-based troposphere (RMT) correction with integrity bound for long baseline DGPS, GPS Solutions, Vol. 9, No. 4, pp. 255–272, 2005.CrossRefGoogle Scholar
  8. 8.
    M. R. Mosavi, Wavelet Neural Network for Corrections Prediction in Single-Frequency GPS Users, GPS Solutions, Vol. 33, No. 2, pp. 137–150, 2011.Google Scholar
  9. 9.
    T. Anagnostopoulos, Ch Anagnostopoulos and S. Hadjiefthymiade, an Adaptive Machine Learning Algorithm for Location Prediction, International Journal of Wireless Information Networks, Vol. 18, No. 2, pp. 88–99, 2001.CrossRefGoogle Scholar
  10. 10.
    M. H. Refan, A. Dameshghi and M. Kamarzarrin, Real Time Pseudo-Range Correction Predicting by a Hybrid GASVM Model in Order to Improve RTDGPS Accuracy, Iranian Journal of Electrical & Electronic Engineering., Vol. 9, No. 4, pp. 215–223, 2013.Google Scholar
  11. 11.
    M. H. Refan and A. Dameshghi, RTDGPS Implementation by Online Prediction of GPS Position Components Error Using GA-ANN Model, Journal of Electrical and Computer Engineering Innovations, Vol. 1, No. 1, pp. 43–50, 2013.Google Scholar
  12. 12.
    M. R. Mosavi and H. Nabavi, Improving DGPS Accuracy using Neural Network Modeling, Australian Journal of Basic and Applied Sciences, Vol. 5, No. 5, pp. 848–856, 2011.Google Scholar
  13. 13.
    D. Jwo, T. Lee and Y. W. Tseng, ARMA Neural Networks for Predicting DGPS Pseudo range Correction, The journal of navigation, Vol. 57, No. 2, pp. 275–286, 2004.CrossRefGoogle Scholar
  14. 14.
    M. H. Refan, A. Dameshghi and M. Kamarzarrin, Improving RTDGPS accuracy using hybrid PSOSVM prediction model, Aerospace Science and Technology, Vol. 37, pp. 55–69, 2014.CrossRefGoogle Scholar
  15. 15.
    M. H. Refan, A. Dameshghi and M. Kamarzarrin, Utilizing Hybrid Recurrent Neural Network and Genetic Algorithm for Predicting the Pseudo-Range Correction Factors to Improve the Accuracy of RTDGPS, Gyroscope and Navigation., Vol. 6, No. 3, pp. 197–206, 2015.CrossRefGoogle Scholar
  16. 16.
    A. Indriyatmoko, T. Y. J. Kang, G. I. Lee, Y. B. Jee and J. Kim, Artificial Neural Network for Predicting DGPS Carrier Phase and Pseudo-Range Correction, GPS Solutions, Vol. 12, No. 4, pp. 237–247, 2008.CrossRefGoogle Scholar
  17. 17.
    V.N. Vapnik, the Nature of Statistical Learning Theory, Springer Verlag, 1995.Google Scholar
  18. 18.
    R. Yuan and B. Guangchen, Determination of Optimal SVM Parameters by Using GAPSO, journal of computers, Vol. 5, No. 8, pp. 1160–1168, 2010.Google Scholar
  19. 19.
    W. Yongli, N. Dongxiao and M. Xiaoyong, Optimizing of SVM with Hybrid PSO and Genetic Algorithm in Power Load Forecasting, journal of networks, Vol. 5, No. 10, pp. 1192–1198, 2010.Google Scholar
  20. 20.
    A. Selakov, D. Cvijetinović, L. Milović, S. Mellon and D. Bekut, Hybrid PSO–SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank, Applied Soft Computing, Vol. 16, pp. 80–88, 2014.CrossRefGoogle Scholar
  21. 21.
    P. P. Feng, H. W. Chiang and L. Y. Shen, Determining Parameters of Support Vector Machines by Genetic Algorithms-Applications to Reliability Prediction, International Journal of Operations Research, Vol. 2, No. 1, pp. 1–7, 2005.MATHGoogle Scholar
  22. 22.
    L. S. Wei, Y. K. Ching, C. S. Chieh and L. Z. Jung, Particle swarm optimization for parameter determination and feature selection of support vector machines, Expert Systems with Applications, Vol. 35, No. 4, pp. 1817–1824, 2008.CrossRefGoogle Scholar
  23. 23.
    Empirical Observation in Iran, A. Abdollahi, H. Aryaei Nejad, A. Nodehi, Genetic Algorithm and Support Vector Machine as Tools for Predicting Corporate Failure and Success, American Journal of Scientific Research, Vol. 55, pp. 119–127, 2012.Google Scholar
  24. 24.
    B. Park, J. Kim and C. Kee, RRC Unnecessary for DGPS Messages, IEEE transactions on aerospace and electronic systems, Vol. 42, No. 3, pp. 1149–1160, 2006.CrossRefGoogle Scholar
  25. 25.
    P. Misra, P. Enge, Global Positioning System–Signals, Measurements, and Performance, Ganga-Jamura Press, 2001, 132-196.Google Scholar
  26. 26.
    M. Berber, A. Ustun and M. Yetkin, Comparison of accuracy of GPS techniques, Measurement, Vol. 45, No. 7, pp. 1742–1746, 2012.CrossRefGoogle Scholar
  27. 27.
    C. W. Hsu and C. J. Lin, A simple decomposition method for support vector machine, Mach. Learn, Vol. 46, No. 1, pp. 219–314, 2002.MathSciNetGoogle Scholar
  28. 28.
    P. Samui, Support vector machine applied to settlement of shallow foundations on cohesion less soils, Computers and Geotechnics, Vol. 35, No. 3, pp. 419–427, 2008.CrossRefMATHGoogle Scholar
  29. 29.
    C. Gao, E. Bompard, R. Napoli and H. Cheng, Price forecast in the competitive electricity market by support vector machine, Physica, A: Statistical Mechanics and its Applications, Vol. 382, No. 1, pp. 98–113, 2007.CrossRefGoogle Scholar
  30. 30.
    L. J. Cao and F. E. H. Tay, Support vector machine with adaptive parameters in financial time series forecasting, IEEE Transactions on Neural Network, Vol. 14, No. 6, pp. 1506–1518, 2003.CrossRefGoogle Scholar
  31. 31.
    C. J. C. Burgers, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, Vol. 2, No. 2, pp. 121–167, 1998.CrossRefGoogle Scholar
  32. 32.
    X. Zhanga and E. A. Amin, Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors, Journal of Molecular Graphics and Modelling, Vol. 63, pp. 22–28, 2016.CrossRefGoogle Scholar
  33. 33.
    H. Drucker, C. Burges, L. Kaufman, A. Smola and V. Vapnik, Support Vector Regression Machines,9 ed., MIT PressCambridge, 1997. pp. 155–161.Google Scholar
  34. 34.
    P. Minqiang, Z. Dehuai and X. U. Gang, Temperature Prediction of Hydrogen Producing reactor using svm regression with pso-svm, journal of computers, Vol. 5, No. 3, pp. 388–393, 2010.Google Scholar
  35. 35.
    M. Nizam, A. Mohamed, M. Al-Dabbagh and A. Hussain, Using Support Vector Machine for Prediction Dynamic Voltage Collapse in an Actual Power System, World Academy of Science, Engineering and Technology, Vol. 41, pp. 710–715, 2008.Google Scholar
  36. 36.
    M. A. Mohandes, T. O. Halawani, S. Rehman and A. A. Hussain, Support vector machines for wind speed prediction, Renewable Energy, Vol. 29, No. 6, pp. 939–947, 2004.CrossRefGoogle Scholar
  37. 37.
    P. J. García Nieto, E. García-Gonzalo, J. R. Alonso Fernández and C. Díaz Muñiz, A hybrid PSO optimized SVM-based model for predicting a successful growth cycle of the Spirulina platensis from raceway experiments data, Journal of Computational and Applied Mathematics, Vol. 291, No. 1, pp. 293–303, 2016.MathSciNetCrossRefMATHGoogle Scholar
  38. 38.
    Y. Wang, Y. Li, Q. Wang and Y. Lv, Computational identification of human long intragenic non-coding RNAs using a GA–SVM algorithm, Journal of Gene, Vol. 533, No. 1, pp. 94–99, 2014.CrossRefGoogle Scholar
  39. 39.
    E. Pourbasheera, S. Riahi, M. R. Ganjali and P. Norouzi, Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity, European Journal of Medicinal Chemistry, Vol. 44, No. 12, pp. 5023–5028, 2009.CrossRefGoogle Scholar
  40. 40.
    i-Lotus GPS Products - M12 M User’s Guide. [Online]. Available: m12m_navigation_oncore.

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Mohammad Hossein Refan
    • 1
  • Adel Dameshghi
    • 1
  • Mehrnoosh Kamarzarrin
    • 2
  1. 1.Faculty of Electrical EngineeringShahid Rajaee Teacher Training UniversityTehranIran
  2. 2.Faculty of Electrical and Computer EngineeringShahid Beheshti UniversityTehranIran

Personalised recommendations