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

Abstract

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.

Road-Side Unit Anomaly Detection

“…Moreover, the authors observed a relationship between vehicle velocity and range and they explain it as a consequence of the Doppler effect. This can also be observed in [13]. The inverse case is, however, observed in [14], where the OD is greater than the ID; however, the authors explain that this is due to the time needed for the mechanism of signal validation in the Wi-Fi access points during a first connection.…”

An Efficient Localization and Avoidance Method of Jammers in Vehicular Ad Hoc Networks

“…Subsequently, its neighbors can build a positioning table of the neighboring nodes [28]. The second approach depends on the physical parameters such as received signal strength (RSS) [29], angle of arrival (AoA) [30], and time of arrival (ToA) [31] in obtaining the jammer location. These RSS-based solutions achieve quite a low accuracy in estimating the jammer's position.…”