5G-Enabled Vehicle Positioning Using EKF With Dynamic Covariance Matrix Tuning

Abstract

The novel signaling and architectural features of 5G promise a major role in providing accurate, precise, and continuous positioning where satellite-based positioning systems may fail. In the case of time-based trilateration, optimal estimators like extended Kalman filter (EKF) can be used to estimate the position with the aid of time-of-arrival (TOA) and round-trip-time (RTT) measurements. However, the linearization of the measurement model used by EKF may lead to positioning errors. Such errors are further magnified due to the narrow geometrical placement of road-side 5G micro base stations (BSs) and due to the closeness of the vehicle to these BSs, leading to significant positioning errors. In this article, the impact of the 5G geometrical setup on the traditional EKF positioning estimation is analyzed. In addition, we propose a dynamically tuned covariance matrix (DTCM) EKF that is automatically tuned based on the measured ranges to trust less the BSs that would lead to high positioning errors. The performance of the proposed method was tested in Siradel’s S_5GChannel simulator that mimics the urban canyons of downtown Toronto. The proposed DTCM-EKF has sustained reliable positioning with sub-meter-level accuracy 90% of the time. The DTCM-EKF has reduced the rms and maximum position error of the EKF by approximately 60% and 67%, respectively.