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A New Algorithm for Navigation Trajectory Prediction of Land Vehicles Based on a Generalised Extended Extrapolation Model

Published online by Cambridge University Press:  13 March 2019

Junna Shang ,
Can Liu ,
Huli Shi ,
Tao Cheng  and
Keqiang Yue
Junna Shang*
Affiliation:
(College of Telecommunication Engineering, Hangzhou Dianzi University, Hangzhou 310018China)
Can Liu
Affiliation:
(College of Telecommunication Engineering, Hangzhou Dianzi University, Hangzhou 310018China)
Huli Shi
Affiliation:
(National Astronomical Observatories of Chinese Academy of Science, Beijing 100012China)
Tao Cheng
Affiliation:
(College of Telecommunication Engineering, Hangzhou Dianzi University, Hangzhou 310018China)
Keqiang Yue
Affiliation:
(College of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018China)
*
(E-mail: [email protected])
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Abstract

Dynamic trajectory prediction is an important topic in the field of navigation and positioning. Due to the drawbacks of a Global Navigation Satellite System (GNSS) receiver, the trajectory of the position always lags behind the dynamic platform's actual position, especially in highly dynamic situations. In order to solve the prediction of a dynamic trajectory, a generalised extension extrapolated model is proposed in this paper. The model utilises the current motion state and a priori position data of the platform, combines the interpolation and fitting method, adds the angle information as a constraint condition and solves the platform position prediction. In this paper, the feasibility of the generalised extended extrapolation algorithm is analysed theoretically and practically. Simulation results show that the prediction error is within 0.2 metres and experimental results show that the algorithm still has high prediction accuracy when a land vehicle platform is turned through a large angle.

Keywords

Location predictionGeneralised extended prediction algorithmPrevious dataTrack extrapolation
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 5 , September 2019 , pp. 1217 - 1232
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

REFERENCES

Annamalai, A., Motwani, A., Sharma, S. K., Sutton, R., Culverhouse, P. and Yang, C. (2015). A robust navigation technique for integration in the guidance and control of an uninhabited surface vehicle. The Journal of Navigation, 68(4), 750–768.Google Scholar
Fujise, M., Kato, A., Sato, K. and Harada, H. (2002) Intelligent Transport Systems. In:Morinaga, N., Kohno, R., Sampei, S. (eds) Wireless Communication Technologies: New Multimedia Systems. The International Series in Engineering and Computer Science, vol 564. 171–200. Springer, Boston, MAGoogle Scholar
Guo, L., Ding, Z., Hu, Z. and Chen, C. (2010). Uncertain Path Prediction of Moving Objects on Road Networks. Journal of Computer Research and Development, 01, 104–112.Google Scholar
Han, S. and Wang, J. (2010). A novel initial alignment scheme for low-cost ins aided by GPS for land vehicle applications. The Journal of Navigation, 63(4), 663–680.Google Scholar
Hwang, C N. (2002). The integrated design of fuzzy collision-avoidance and H[infty infinity]-autopilots on ships. The Journal of Navigation, 55(1), 117–136.Google Scholar
Jeung, H., Liu, Q., Shen, H.T. and Zhou, X.F. (2008). A hybrid prediction model for moving objects. IEEE 24th International Conference on Data Engineering, Cancun, Mexico,70–79.Google Scholar
Kim, S.W., Won, J.I., Kim, J.D., Shin, M., Lee, J. and Kim, H. (2007). Path prediction of moving objects on road networks through analyzing past trajectories. International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. Vietri sul Mare, Italy. Springer Berlin Heidelberg, 379–389.Google Scholar
Li, L. (2013). Study and Implementation of Trajectory Prediction Based on WiFi Positioning. MSc. Dissertation, Xidian University.Google Scholar
Li, X., Guo, R., Huang, J., Zhu, L., Tang, H. and Dong, E. (2015). Application of Artificial Neural Network to Orbit Prediction of BeiDou Navigation Satellites. Geomatics and Information Science of Wuhan University, 40(9), 1253–1258.Google Scholar
Lin, X. and Ju, J. (2011). Neural Network Prediction Research of GPS/SINS Integrated Navigation System. Geomatics and Information Science of Wuhan University, 36(5), 601–604.Google Scholar
Liu, X. and Karimi, H.A. (2006). Location awareness through trajectory prediction. Computers, Environment and Urban Systems, 30(6), 741–756.Google Scholar
Liu, Z. (2016). Research on Key Technologies for The Analysis and Forecast of Urban Traffic Operation State. Doctoral dissertation, School of Energy Science and Engineering. University of Electronic Science and Technology of China.Google Scholar
Morzy, M. (2006). Prediction of moving object location based on frequent trajectories. International Symposium on Computer and Information Sciences. Istanbul, Turkey, Springer Berlin Heidelberg, 583–592.Google Scholar
Qiao, S., Jin, K., Han, N., Tang, C., Gesangduoji and Gutierrez, L.A. (2015a). Trajectory Prediction Algorithm Based on Gaussian Mixture Model. Journal of Software, 26(05), 1048–1063.Google Scholar
Qiao, S., Shen, D., Wang, X., Han, N. and Zhu, W. (2015b). A self-adaptive parameter selection trajectory prediction approach via hidden Markov models. IEEE Transactions on Intelligent Transportation Systems, 16(1), 284–296.Google Scholar
Retscher, G. and Kealy, A. (2006). Ubiquitous positioning technologies for modern intelligent navigation systems. The Journal of Navigation, 59(1), 91–103.Google Scholar
Shang, J., Cheng, T., Sheng, L., Shi, H. and Yue, K. (2016). Application of Generalized Extended Interpolation Method in Distance Measurement Based on RSSI. Chinese Journal of Sensors and Actuators, 29(11), 1768–1772.Google Scholar
Song, C. and Barabási, A.L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021.Google Scholar
Tang, L., Xue, Y., Chen, J., Zhang, L. and Qingquan, L. (2017). Traffic Lane Number Extraction Based on the Constrained Gaussian Mixture Model. Geomatics and Information Science of Wuhan University, 42(3), 341–347.Google Scholar
Tian, Q., Salcic, Z., Wang, I.K. and Pan, Y. (2016). A multi-mode dead reckoning system for pedestrian tracking using smartphones. IEEE Sensors Journal, 16(7), 2079–2093.Google Scholar
Wang, C., Wang, H., Xiong, W. and He, Y. (2016). Data association algorithm based on least square fitting. Acta Aeronautica et Astronautica Sinica, 05, 1603–1613.Google Scholar
Wang, Y., Cai, C., Li, S. and Yu, H. (2017). The research on indoor positioning algorithm based on pedestrian dead reckoning. Application of Electronic Technique,43(04), 86–89–93.Google Scholar
Wu, F. and Nie, J. and He, Z. (2012). Classified Adaptive Filtering to GPS/INS Integrated Navigation Based on Predicted Residuals and Selecting Weight Filtering. Geomatics and Information Science of Wuhan University, 37(3), 261–264.Google Scholar
Xiao, Z., Wen, H., Markham, A. and Trigoni, N. (2015). Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement. International Conference on Indoor Positioning and Indoor Navigation. Busan, South Korea. IEEE, 187–196.Google Scholar
Ye, Q. (2006). Practical Mathematics Handbook. Science Press.Google Scholar