Breaking the Gridlock of Spatial Correlations in GPS-Aided IEEE 802.11p-Based Cooperative Positioning.
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| Title: | Breaking the Gridlock of Spatial Correlations in GPS-Aided IEEE 802.11p-Based Cooperative Positioning. |
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| Authors: | Hoang, Gia-Minh1, Denis, Benoit1, Harri, Jerome2, Slock, Dirk T. M.2 |
| Source: | IEEE Transactions on Vehicular Technology. Dec2016, Vol. 65 Issue 12, p9554-9569. 16p. |
| Subjects: | Intelligent transportation systems, Multisensor data fusion, Global Positioning System, Vehicular ad hoc networks, Monte Carlo method |
| Abstract: | Spatial correlations found in vehicular mobility are jeopardizing the precision level of cooperative positioning for future Cooperative Intelligent Transport System (C-ITS) applications. Bayesian filters traditionally assume independence of the measurement noise terms over space between different vehicles and over time at each vehicle, whereas they are actually correlated due to the local continuity of physical propagation phenomena (e.g., shadowing, multipath, etc.) under highly constrained vehicular mobility. In this paper, we break this gridlock by proposing an innovative data fusion framework capable of mitigating these effects to maintain the positioning precision level under severely correlated environments. We first illustrate the dramatic impact of correlated noise affecting both global positioning system and vehicle-to-vehicle (V2V) received power observations. Then, we propose a new generic data fusion framework based on a particle filter supporting three complementary methods to decorrelate measurement noises in a globally asynchronous context. Compared with conventional cooperative positioning, simulations performed in canonical vehicular scenarios (highway, urban canyon, tunnel) show that our proposed approach could provide up to 60% precision improvement in correlated environments, while matching by less than 15–20% deviation, an optimal cooperative positioning scheme considered under independent measurements. [ABSTRACT FROM PUBLISHER] |
| Copyright of IEEE Transactions on Vehicular Technology is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 120283901 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Breaking the Gridlock of Spatial Correlations in GPS-Aided IEEE 802.11p-Based Cooperative Positioning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hoang%2C+Gia-Minh%22">Hoang, Gia-Minh</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Denis%2C+Benoit%22">Denis, Benoit</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Harri%2C+Jerome%22">Harri, Jerome</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Slock%2C+Dirk+T%2E+M%2E%22">Slock, Dirk T. M.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Vehicular+Technology%22">IEEE Transactions on Vehicular Technology</searchLink>. Dec2016, Vol. 65 Issue 12, p9554-9569. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Global+Positioning+System%22">Global Positioning System</searchLink><br /><searchLink fieldCode="DE" term="%22Vehicular+ad+hoc+networks%22">Vehicular ad hoc networks</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Spatial correlations found in vehicular mobility are jeopardizing the precision level of cooperative positioning for future Cooperative Intelligent Transport System (C-ITS) applications. Bayesian filters traditionally assume independence of the measurement noise terms over space between different vehicles and over time at each vehicle, whereas they are actually correlated due to the local continuity of physical propagation phenomena (e.g., shadowing, multipath, etc.) under highly constrained vehicular mobility. In this paper, we break this gridlock by proposing an innovative data fusion framework capable of mitigating these effects to maintain the positioning precision level under severely correlated environments. We first illustrate the dramatic impact of correlated noise affecting both global positioning system and vehicle-to-vehicle (V2V) received power observations. Then, we propose a new generic data fusion framework based on a particle filter supporting three complementary methods to decorrelate measurement noises in a globally asynchronous context. Compared with conventional cooperative positioning, simulations performed in canonical vehicular scenarios (highway, urban canyon, tunnel) show that our proposed approach could provide up to 60% precision improvement in correlated environments, while matching by less than 15–20% deviation, an optimal cooperative positioning scheme considered under independent measurements. [ABSTRACT FROM PUBLISHER] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Vehicular Technology is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TVT.2016.2599490 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 9554 Subjects: – SubjectFull: Intelligent transportation systems Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Global Positioning System Type: general – SubjectFull: Vehicular ad hoc networks Type: general – SubjectFull: Monte Carlo method Type: general Titles: – TitleFull: Breaking the Gridlock of Spatial Correlations in GPS-Aided IEEE 802.11p-Based Cooperative Positioning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hoang, Gia-Minh – PersonEntity: Name: NameFull: Denis, Benoit – PersonEntity: Name: NameFull: Harri, Jerome – PersonEntity: Name: NameFull: Slock, Dirk T. M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 00189545 Numbering: – Type: volume Value: 65 – Type: issue Value: 12 Titles: – TitleFull: IEEE Transactions on Vehicular Technology Type: main |
| ResultId | 1 |
