The position of mobile devices is determined by Real Time Differential Global Positioning System (RTDGPS). This system is composed of fixed and mobile station. The accuracy of this system depends on the speed of updating the correction data. Yet, this system often faces the problem of the reference GPS receiver’s signal cut off and internal or non- internal delays which increase the latency time and decrease the updating speed. The prediction models are used to compensate these delays. In this paper, the Particle Swarm Optimization Least Square Wavelet Support Vector Machine (PSO-LSWSVM) is used for predicting the DGPS correction. An experimental setup is designed in order to carry out the operations of reference and user stations. Low-cost receivers were used in both stations. The accuracy of PSO-LSWSVM was evaluated through various simulations and experimental. The practical tests showed that the accuracy of the designed RTDGPS is 0.42 m with PSO-LSWSVM and is equal to 0.73 m without it. In comparing with other method that recently is introduced; it has a higher positioning accuracy.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Dahlea, C., Arnold, D., & Jäggi, A. (2017). Impact of tracking loop settings of the Swarm GPS receiver on gravity field recovery. Advances in Space Research,49(12), 2843–2854.
Ning, F. Sh., et al. (2007). A simulation of the effect of GPS pseudolite observations on the obstructed sky view. Survey Review,39(303), 34–42.
Abd-Rabbou, M., & El-Rabbany, A. (2015). Performance analysis of precise point positioning using multi-constellation GNSS: GPS, GLONASS, Galileo and BeiDou. Survey Review,49(352), 39–50.
Kutoglu, H. S. (2009). Direct determination of local coordinates from GPS without transformation. Survey Review,41(312), 162–173.
McDonald, K. D. (2002). The modernization of GPS: plans, new capabilities, and the future. Journal of Global Positioning System,1(1), 1–17.
Zandbergen, P. A., & Arnold, L. L. (2011). Positional accuracy of the wide area augmentation system in consumer-grade GPS units. Computers & Geosciences,37(7), 883–892.
Ordónez, G. C., et al. (2011). Analysis of the influence of forest environments on the accuracy of GPS measurements by using genetic algorithms. Mathematical and Computer Modelling,54(7–8), 1829–1834.
Wing, M. G., & Frank, J. (2011). Vertical measurement accuracy and reliability of mapping-grade GPS receivers. Computers and Electronics in Agriculture,78(2), 188–194.
Smith, M. S., & Ladde, G. S. (1989). Processing of filtered GPS data. IEEE Transactions on Aerospace and Electronic Systems,25(5), 711–728.
Øvstedal, O. (2002). Absolute positioning with single-frequency GPS receivers. Journal of GPS Solutions,5(4), 33–44.
Deergha, R. K. (1998). An approach for accurate GPS navigation with SA. IEEE Transactions on Aerospace and Electronic Systems,34(2), 695–699.
Michalski, A., & Czajewski, J. (2004). The accuracy of the global positioning systems. IEEE Instrumentation and Measurement Magazine,7, 56–60.
Peters, R. T., & Evett, S. R. (2005). Using low cost GPS receivers for determining field position of mechanized irrigation systems. Applied Engineering in Agriculture,21(5), 841–845.
Alonso-Garcia, S., Gomez-Gil, J., & Arribas, J. I. (2011). Evaluation of the use of low-cost GPS receivers in the autonomous guidance of agricultural tractors. Spanish Journal of Agricultural Research,9(2), 377–388.
Kelly, R. J., & Davis, J. M. (1998). Required navigation performance (RNP) for precision approach and landing with GNSS application. Journal of the Institute of Navigation,41(1), 1–30.
Kremer, G. T., et al. (1990). The effect of selective availability on differential GPS corrections. Journal of The Institute of Navigation,37(1), 39–52.
Zhang, J., et al. (2006). GPS satellite velocity and acceleration determination using the broadcast ephemeris. The Journal of Navigation,59, 293–305.
Montenbruck, O., et al. (2005). Reduced dynamic orbit determination using GPS code and phase measurements. Aerospace Science and Technology,9(3), 261–271.
Mohasseb, M., et al. (2007). DGPS correction prediction using artificial neural networks. The Journal of Navigation,60(2), 291–301.
Yuheng, H., Rainer, M., & Attila, B. (2014). Scalable low-complexity GPS and DGPS positioning using approximate QR decomposition. Signal Processing,94, 445–455.
Mosavi, M. R. (2004). A wavelet based neural network for DGPS corrections prediction. WSEAS Transactions on Systems,3(10), 3070–3075.
Jwo, D., Lee, T., & Tseng, Y. W. (2004). ARMA neural networks for predicting DGPS pseudo range correction. The Journal of Navigation,57(2), 275–286.
Refan, M. H., Dameshghi, A., & Kamarzarrin, M. (2016). Real-time differential global poisoning system stability and accuracy improvement by utilizing support vector machine. International Journal of Wireless Information Networks,23(1), 66–81.
Refan, M. H., Dameshghi, A., & Kamarzarrin, M. (2016). Implementation of DGPS reference and user stations based on RPCE factors. Wireless Personal Communications,90(4), 1597–1617.
Park, B., Kim, J., & Kee, C. (2006). RRC unnecessary for DGPS messages. IEEE Transactions on Aerospace and Electronic Systems,42(3), 1149–1160.
Indriyatmoko, A., et al. (2008). Artificial neural network for predicting DGPS carrier phase and pseudo-range correction. Journal of GPS Solutions,12(4), 237–247.
RTCM recommended standards for differential GNSS (global navigation satellite systems) service version 2.2. Developed by RTCM special committee NO. 104. January 15, 1998. Retrieved Sep 11, 2017 from http://www.rtcm.org/differential-global-navigation-satellite–dgnss–standards.html.
Refan, M. H., Dameshghi, A., & Kamarzarrin, M. (2015). Utilizing hybrid recurrent neural network and genetic algorithm for predicting the pseudo-range correction factors to improve the accuracy of RTDGPS. Gyroscopy and Navigation,6(3), 197–206.
Refan, M. H., Dameshghi, A., & Kamarzarrin, M. (2014). Improving RTDGPS accuracy using hybrid PSOSVM prediction model. Aerospace Science and Technology,37, 55–69.
Mosavi, M. R., & Nabavi, H. (2011). Improving DGPS accuracy using neural network modelling. Australian Journal of Basic and Applied Sciences,5(5), 848–856.
Refan, M. H., Dameshghi, A., & Kamarzarrin, M. (2013). Real time pseudo-range correction predicting by a hybrid GASVM model in order to improving RTDGPS accuracy. Iranian Journal of Electrical and Electronic Engineering (IJEEE),9(4), 215–223.
Refan, M. H., & Dameshghi, A. (2013). RTDGPS implementation by online prediction of GPS position components error using GA-ANN model. Journal of Electrical and Computer Engineering Innovations,1(1), 43–50.
Mosavi, M. R. (2006). Comparing DGPS Ccorrections prediction using neural network, fuzzy neural network, and kalman filter. Journal of GPS Solutions,10(2), 97–107.
Mosavi, M. R. (2010). Estimation of pseudo-range DGPS Corrections using neural networks trained by evolutionary algorithms. Journal of Review of Electrical Engineering,5(6), 2715–2721.
Geng, Y., 2007. Online DGPS correction prediction using recurrent neural networks with unscented Kalman filter. In International global navigation satellite systems society IGNSS symposium, 4–6 December 2007 Sydney, Australia: The University of New South Wales.
Suykens, J., & Gestel, T. (2004). Benchmarking least squares support vector machine classifiers. Machine Learning,54(1), 5–32.
Zhang, N., & Shetty, D. (2016). An effective LS-SVM-based approach for surface roughness prediction in machined surfaces. Neurocomputing,198, 35–39.
Dong, Sh., & Luo, T. (2013). Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement,46(9), 3143–3152.
Zhang, Y. Y., et al. (2016). Forecasting of dissolved gases in oil-immersed transformers based upon wavelet LS-SVM regression and PSO with mutation. Energy Procedia,104, 38–43.
ZigBee Serial Adapter ProBee-ZS10 [Online]. User Guide Sena Technology. Retrieved Sept 11, 2017c from http://hellodevice.nl/en/products/probee/zs10/.
i-Lotus GPS Products [Online]. M12 M User’s Guide. Retrieved Sept 11, 2017b from http://www.ilotus.com.sg/m12m_navigation_oncore.
U-blox 6 Receiver Description Including Protocol Specification, [Online]. Retrieved Sept 11, 2017a from https://www.u-blox.com/en/standard-precision-gnss-modules?utm_source=en/gpsmodules.html&utm_medium=Redirect&utm_content=Wiki%20Redirect&utm_campaign=Redirect%20to%20ew%20u-blox%20website.
Zhang, L., Zhou, W., & Jiao, L. (2004). Wavelet support vector machine. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics,34(1), 34–39.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Refan, M.H., Dameshghi, A. & Kamarzarrin, M. RTDGPS Accuracy Improvement Using PSO-LSWSVM and Low-Cost GPS Receivers. Wireless Pers Commun 111, 111–142 (2020). https://doi.org/10.1007/s11277-019-06848-3
- Pseudo range corrections
- Low-cost GPS receiver