| 1. |
- Ma, Sugang, et al.
(författare)
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An Efficient V2X Based Vehicle Localization Using Single RSU and Single Receiver
- 2019
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Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 7, s. 46114-46121
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Tidskriftsartikel (refereegranskat)abstract
- High accuracy vehicle localization information is critical for intelligent transportation systems and future autonomous vehicles. It is challenging to achieve the required centimeter-level localization accuracy, especially in urban or global navigation satellite system denied environments. Here we propose a vehicle-to-infrastructure (V2I)-based vehicle localization algorithm. First, it is low-cost and hardware requirements are simplified, the minimum requirement is a single roadside unit and single on-board receiver. Second, it is computationally efficient, the available V2I information is formulated as an over-determined system. Then, the vehicle position is estimated in a closed-form manner via the widely used weighted linear least squares (WLLS) method and meter level accuracy is achievable. Furthermore, the numerical performance of WLLS is consistent with the theoretical results in larger signal-to-noise ratio region.
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| 2. |
- Wen, Fuxi, 1982, et al.
(författare)
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A survey on 5G massive MIMO Localization
- 2019
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Ingår i: Digital Signal Processing: A Review Journal. - : Elsevier BV. - 1051-2004 .- 1095-4333. ; 94:November 2019, s. 21-28
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Tidskriftsartikel (refereegranskat)abstract
- Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field.
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| 3. |
- Xiao, Zhongyang, et al.
(författare)
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A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles
- 2019
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 19:9
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Tidskriftsartikel (refereegranskat)abstract
- Future intelligent transport systems depend on the accurate positioning of multiple targets in the road scene, including vehicles and all other moving or static elements. The existing self-positioning capability of individual vehicles remains insufficient. Also, bottlenecks in developing on-board perception systems stymie further improvements in the precision and integrity of positioning targets. Vehicle-to-everything (V2X) communication, which is fast becoming a standard component of intelligent and connected vehicles, renders new sources of information such as dynamically updated high-definition (HD) maps accessible. In this paper, we propose a unified theoretical framework for multiple-target positioning by fusing multi-source heterogeneous information from the on-board sensors and V2X technology of vehicles. Numerical and theoretical studies are conducted to evaluate the performance of the framework proposed. With a low-cost global navigation satellite system (GNSS) coupled with an initial navigation system (INS), on-board sensors, and a normally equipped HD map, the precision of multiple-target positioning attained can meet the requirements of high-level automated vehicles. Meanwhile, the integrity of target sensing is significantly improved by the sharing of sensor information and exploitation of map data. Furthermore, our framework is more adaptable to traffic scenarios when compared with state-of-the-art techniques.
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