Evidence map›Paper›PMID 38257571›Full record

ArticleSensors (Basel, Switzerland)2024

A Fusion Strategy for Vehicle Positioning at Intersections Utilizing UWB and Onboard Sensors.

Huaikun Gao, Xu Li, Xiang Song

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Huaikun GaoSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Xu LiSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Xiang SongSchool of Electronic Engineering, Nanjing Xiaozhuang University, Nanjing 211171, China.ORCID 0000-0002-1704-4339

Funding

the Future Network Scientific Research Fund Project No. FNSRFP-2021-YB-29the National Key Research and Development Program of China No. 2017YFC0804804the National Natural Science Foundation of China No.61973079the National Science Foundation for Distinguished Young Scholars of China No.41904024the Natural Science Foundation of the Jiangsu Higher Education Institutions of China No. 18KJB413007the Program for Special Talents in Six Major Fields of Jiangsu Province No. 2017 JXQC-003
6 · The paper itself

Abstract

For vehicle positioning applications in Intelligent Transportation Systems (ITS), lane-level or even more precise localization is desired in some typical urban scenarios. With the rapid development of wireless positioning technologies, ultrawide bandwidth (UWB) has stood out and become a prominent approach for high-precision positioning. However, in traffic scenarios, the UWB-based positioning method may deteriorate because of not-line-of-sight (NLOS) propagation, multipath effect and other external interference. To overcome these problems, in this paper, a fusion strategy utilizing UWB and onboard sensors is developed to achieve reliable and precise vehicle positioning. It is a two-step approach, which includes the preprocessing of UWB raw measurements and the global estimation of vehicle position. Firstly, an ARIMA-GARCH model to address the NLOS problem of UWB at vehicular traffic scenarios is developed, and then the NLOS of UWB can be detected and corrected efficiently. Further, an adaptive IMM algorithm is developed to realize global fusion. Compared with traditional IMM, the proposed AIMM is capable of adjusting the model probabilities to make them better matching for current driving conditions, then positioning accuracy can be improved. Finally, the method is validated through experiments. Field test results verify the effectiveness and feasibility of the proposed strategy.

Indexed as

AIMMARIMA–GARCHNLOSonboard sensorsurban scenariosUWBvehicle positioning

Identifiers

PMID38257571
PMCPMC11154252

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.