Vehicle Localization Using Doppler Shift and Time of Arrival Measurements in a Tunnel Environment

Most applications and services of Cooperative Intelligent Transport Systems (C-ITS) rely on accurate and continuous vehicle location information. The traditional localization method based on the Global Navigation Satellite System (GNSS) is the most commonly used. However, it does not provide reliabl...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-01, Vol.22 (3), p.847
Main Authors: Halili, Rreze, BniLam, Noori, Yusuf, Marwan, Tanghe, Emmeric, Joseph, Wout, Weyn, Maarten, Berkvens, Rafael
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial satellites
Bandwidths
C-ITS
Communication
Doppler effect
Extended Kalman filter
Frequencies
Global positioning systems
GNSS
GPS
Ground stations
Intelligent transportation systems
Localization
Localization method
Propagation
Satellites
Sensors
Signal processing
Time measurement
Traffic
Traffic speed
Transmitters
tunnel
Tunnels
Vehicles
Wireless networks
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Summary:Most applications and services of Cooperative Intelligent Transport Systems (C-ITS) rely on accurate and continuous vehicle location information. The traditional localization method based on the Global Navigation Satellite System (GNSS) is the most commonly used. However, it does not provide reliable, continuous, and accurate positioning in all scenarios, such as tunnels. Therefore, in this work, we present an algorithm that exploits the existing Vehicle-to-Infrastructure (V2I) communication channel that operates within the LTE-V frequency band to acquire in-tunnel vehicle location information. We propose a novel solution for vehicle localization based on Doppler shift and Time of Arrival measurements. Measurements performed in the Beveren tunnel in Antwerp, Belgium, are used to obtain results. A comparison between estimated positions using Extended Kalman Filter (EKF) on Doppler shift measurements and individual Kalman Filter (KF) on Doppler shift and Time of Arrival measurements is carried out to analyze the filtering methods performance. Findings show that the EKF performs better than KF, reducing the average estimation error by 10 m, while the algorithm accuracy depends on the relevant RF channel propagation conditions and other in-tunnel-related environment knowledge included in the estimation. The proposed solution can be used for monitoring the position and speed of vehicles driving in tunnel environments.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22030847