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

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Abstract

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.

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Abbreviations

ANN:

Artificial neural network

ARMA:

Autoregressive moving average

ERM:

Empirical risk minimization

GA:

Genetic algorithm

GPS:

Global positioning system

GS:

Grid search

MCM:

Model complexity minimization

NMEA:

National Marine Electronics Association

PR:

Pseudo-range

PRC:

Pseudo range correction

PSO:

Particle swarm optimization

RBF:

Radial basis function

RRC:

Range rate correction

RTCM:

Radio Technical Commission for Maritime Services

RTDGPS:

Real Time Differential Global Poisoning System

SVM:

Support vector machine

SVR:

Support vector regression

TTL:

Transistor–transistor logic

References

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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. 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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  17. V.N. Vapnik, the Nature of Statistical Learning Theory, Springer Verlag, 1995.

  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. 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. 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.

    Article  Google Scholar 

  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.

    MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  25. P. Misra, P. Enge, Global Positioning System–Signals, Measurements, and Performance, Ganga-Jamura Press, 2001, 132-196.

  26. M. Berber, A. Ustun and M. Yetkin, Comparison of accuracy of GPS techniques, Measurement, Vol. 45, No. 7, pp. 1742–1746, 2012.

    Article  Google Scholar 

  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.

    MathSciNet  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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. 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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  40. i-Lotus GPS Products - M12 M User’s Guide. [Online]. Available: http://www.ilotus.com.sg/ m12m_navigation_oncore.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

    Mohammad Hossein Refan & Adel Dameshghi

  2. Faculty of Electrical and Computer Engineering, Shahid Beheshti University, Tehran, Iran

    Mehrnoosh Kamarzarrin

Authors
  1. Mohammad Hossein Refan
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  2. Adel Dameshghi
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  3. Mehrnoosh Kamarzarrin
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Correspondence to Mohammad Hossein Refan.

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Refan, M.H., Dameshghi, A. & Kamarzarrin, M. Real-Time Differential Global Poisoning System Stability and Accuracy Improvement by Utilizing Support Vector Machine. Int J Wireless Inf Networks 23, 66–81 (2016). https://doi.org/10.1007/s10776-016-0295-2

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