Title: Radio-based vehicle dynamic tracking in GNSS degraded environments

Authors: Jianqi Liu; Xiuwen Yin; Bi Zeng; Hui Zhang; Wei He

Addresses: School of Automation, Guangdong University of Technology, Guangzhou, 510006, China ' School of Automation, Guangdong University of Technology, Guangzhou, 510006, China ' School of Computer, Guangdong University of Technology, Guangzhou, 510006, China ' School of Automation, Guangdong University of Technology, Guangzhou, 510006, China ' School of Electronic and Communication, Guangdong Mechanical and Electrical College, Guangzhou, 510515, China

Abstract: The vehicular position information plays an important role in the vehicle communication. In open environments without signal blockage, global navigation satellite system (GNSS) has achieved on-road level accuracy and good reliability, However, the vehicle dynamic tracking is difficult in GNSS degraded environments. Usually, vehicle dynamic tracking consists of three portions: static positioning system, manoeuvring model and fusion algorithm. A radiobased positioning system aided by roadside units (RSUs) is employed as static positioning system, while the adaptive Kalman filter algorithm is used as fusion algorithm. In this paper, a new manoeuvring model is studied. Firstly, the problem of 'current' model is analysed. Secondly, a 'current-ellipse' manoeuvring model is proposed to adapt the strong and weak manoeuvring simultaneously. The experiments show that it can improve the tracking accuracy. Finally, a vehicle tracking case is discussed, its results show that root mean square error (RMSE) is smaller than 2 m, and the positioning accuracy can meet the position-based vehicular communication.

Keywords: vehicle dynamic tracking; manoeuvring model; radio-based positioning system; current-ellipse model; GNSS degraded environment; target tracking.

DOI: 10.1504/IJSNET.2020.106881

International Journal of Sensor Networks, 2020 Vol.33 No.1, pp.42 - 54

Received: 19 Apr 2019
Accepted: 19 Nov 2019
Published online: 24 Apr 2020 *

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