期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 23, 页码 17014-17028出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3075532
关键词
Internet of Vehicles (IoV); multiple particle filter (MPF); non-LoS (NLoS); ultradense networks (UDN); vehicle positioning; vehicle to everything (V2X)
资金
- Beijing Municipal Science and Technology [Z181100003218008]
This article proposes a V2X-integrated positioning methodology using 5G technology and ultra-dense networks, combining V2I, V2V, and INSs for data fusion to improve vehicle positioning accuracy in urban areas. By introducing the GK algorithm based on network densification characteristics, a performance gain of 18.7% was achieved compared to traditional methods.
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 K-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.
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