A Single-Site Vehicle Positioning Method in the Rectangular Tunnel Environment

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 2; p. 527
• Main Authors: Jiang, Suying, Wang, Wei, Peng, Peng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2023
• Subjects: Accuracy; Algorithms; Communication; Cramer-Rao bounds; Direction of arrival; Extended Kalman filter; Filters; Global positioning systems; GPS; Line of sight; Localization; Lower bounds; Magnetic fields; Methods; Microelectromechanical systems; Propagation; Radio frequency identification; Roads & highways; Sensors; single-site vehicle positioning; time-of-arrival; Traffic accidents & safety; tunnel; Tunnels; Vehicles; virtual station
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15020527

Due to the satellite signals are blocked, it is difficult to obtain the vehicle position in the tunnels. We propose a single-site vehicle localization scheme for the rectangular tunnel environment, where most satellite-based positioning methods can not provide the required localization accuracy. In the non-line-of-sight (NLOS) scenarios, we make use of the reflection paths as assistants for vehicle positioning. Specifically, first, the virtual stations are established based on the actual geometrical structure of the tunnel. Second, we use the direction-of-arrival (DOA) and time-of-arrival (TOA) information of reflection paths from two tunnel walls to achieve vehicle positioning. Especially, the Cramer-Rao lower bound (CRLB) of the joint TOA and DOA localization for NLOS propagations in a two-dimensional (2D) space is derived. In addition, based on the localization algorithms with and without filters, we assess the localization performance. In the line-of-sight (LOS) scenarios, we use the LOS path and two reflection paths from the tunnel walls to estimate the vehicle location. First, virtual base stations are established. Second, based on the obtained TOA information, different positioning algorithms are used to estimate the vehicle location. Simulation results illustrate that the proposed positioning approach can provide a small root mean square error. The localization algorithms using filters improve the localization accuracy, compared with the positioning algorithm without using filters, namely, the two-stage weighted least squares (TSWLS) algorithm. Moreover, the Unscented Particle Filter (UPF) algorithm achieves better positioning accuracy than other methods (i.e., Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF), TSWLS algorithms).