A V2X-Integrated Positioning Methodology in Ultradense Networks

Abstract

Intelligent transport systems demand the provision of a continuous high-accuracy positioning service. However, a vehicle positioning system typically has to operate in dense urban areas where conventional satellite-based positioning systems suffer severe performance degradation. 5G technology presents a new paradigm to provide ubiquitous connectivity, where the vehicle-to-everything (V2X) communication turns out to be highly conducive to enable both accurate positioning and the emerging Internet of Vehicles (IoV). Due to the high probability of Line-of-Sight (LoS) communication, as well as the diversity and number of reference stations, the application of ultradense networks (UDN) in the vehicle-to-infrastructure (V2I) subsystem is envisaged to complement the existing positioning technologies. Moreover, the cooperative determination of location information could be enhanced by the vehicle-to-vehicle (V2V) subsystem. In this article, we propose a V2X-integrated positioning methodology in UDN, in which the V2I, V2V, and inertial navigation systems (INSs) are unified for data fusion. This formulation is an iterative high-dimensional estimation problem, and an efficient multiple particle filter (MPF)-based method is proposed for solving it. In order to mitigate the non-LoS (NLoS) impact and provide a relatively accurate input to the MPF, we introduce an advanced anchor selection method using the geometry-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means clustering (GK) algorithm based on the characteristics of network densification. Numerical results demonstrate that utilizing the GK algorithm in the proposed integrated positioning system could achieve 18.7% performance gains in accuracy, as compared with a state-of-the-art approach.