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Open AccessArticle

Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks

1
Computer Science Department, Federal University of Roraima, Boa Vista 69310-000, Brazil
2
Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON L1G 0C5, Canada
3
Institute of Computing, Federal University of Amazonas, Manaus 69080-900, Brazil
4
Institute of Computing, University of Campinas, Campinas 13083-852, Brazil
5
Department of Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5231; https://doi.org/10.3390/s19235231
Received: 3 October 2019 / Revised: 15 November 2019 / Accepted: 20 November 2019 / Published: 28 November 2019
(This article belongs to the Collection Positioning and Navigation)
In vehicular ad hoc networks (VANets), a precise localization system is a crucial factor for several critical safety applications. The global positioning system (GPS) is commonly used to determine the vehicles’ position estimation. However, it has unwanted errors yet that can be worse in some areas, such as urban street canyons and indoor parking lots, making it inaccurate for most critical safety applications. In this work, we present a new position estimation method called cooperative vehicle localization improvement using distance information (CoVaLID), which improves GPS positions of nearby vehicles and minimize their errors through an extended Kalman filter to execute Data Fusion using GPS and distance information. Our solution also uses distance information to assess the position accuracy related to three different aspects: the number of vehicles, vehicle trajectory, and distance information error. For that purpose, we use a weighted average method to put more confidence in distance information given by neighbors closer to the target. We implement and evaluate the performance of CoVaLID using real-world data, as well as discuss the impact of different distance sensors in our proposed solution. Our results clearly show that CoVaLID is capable of reducing the GPS error by 63%, and 53% when compared to the state-of-the-art VANet location improve (VLOCI) algorithm. View Full-Text
Keywords: vehicular ad hoc networks; localization systems; data fusion; distance information vehicular ad hoc networks; localization systems; data fusion; distance information
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MDPI and ACS Style

Lobo, F.; Grael, D.; Oliveira, H.; Villas, L.; Almehmadi, A.; El-Khatib, K. Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks. Sensors 2019, 19, 5231. https://doi.org/10.3390/s19235231

AMA Style

Lobo F, Grael D, Oliveira H, Villas L, Almehmadi A, El-Khatib K. Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks. Sensors. 2019; 19(23):5231. https://doi.org/10.3390/s19235231

Chicago/Turabian Style

Lobo, Felipe; Grael, Danilo; Oliveira, Horacio; Villas, Leandro; Almehmadi, Abdulaziz; El-Khatib, Khalil. 2019. "Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks" Sensors 19, no. 23: 5231. https://doi.org/10.3390/s19235231

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