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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: shangjn@hdu.edu.cn)
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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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