| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2020-11-16 |
| 書誌情報 |
en : IEEE INTERNET OF THINGS JOURNAL
巻 6,
号 3,
p. 5766-5777,
発行日 2019
|
| タイトル |
|
|
タイトル |
Assistant Vehicle Localization Based on Three Collaborative Base Stations via SBL-Based Robust DOA Estimation |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Base station (BS) |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
direction-of-arrival (DOA) estimation |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
nonuniform noise |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
off-grid error |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
sparse Bayesian learning (SBL) |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
vehicle localization |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
WANG, Huafei
WAN, Liangtian
董, 冕雄
太田, 香
WANG, Xianpeng
|
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
董 冕雄（DONG Mianxiong） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
太田 香（OTA Kaoru） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
As a promising research area in Internet of Things (IoT), Internet of Vehicles (IoV) has attracted much attention in wireless communication and network. In general, vehicle localization can be achieved by the global positioning systems (GPSs). However, in some special scenarios, such as cloud cover, tunnels or some places where the GPS signals are weak, GPS cannot perform well. The continuous and accurate localization services cannot be guaranteed. In order to improve the accuracy of vehicle localization, an assistant vehicle localization method based on direction-of-arrival (DOA) estimation is proposed in this paper. The assistant vehicle localization system is composed of three base stations (BSs) equipped with a multiple input multiple output (MIMO) array. The locations of vehicles can be estimated if the positions of the three BSs and the DOAs of vehicles estimated by the BSs are known. However, the DOA estimated accuracy maybe degrade dramatically when the electromagnetic environment is complex. In the proposed method, a sparse Bayesian learning (SBL)-based robust DOA estimation approach is first proposed to achieve the off-grid DOA estimation of the target vehicles under the condition of nonuniform noise, where the covariance matrix of nonuniform noise is estimated by a least squares (LSs) procedure, and a grid refinement procedure implemented by finding the roots of a polynomial is performed to refine the grid points to reduce the off-grid error. Then, according to the DOA estimation results, the target vehicle is cross-located once by each two BSs in the localization system. Finally, robust localization can be realized based on the results of three-time cross-location. Plenty of simulation results demonstrate the effectiveness and superiority of the proposed method. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
|
言語 |
en |
| 出版者版へのリンク |
|
|
表示名 |
10.1109/JIOT.2019.2905788 |
|
URL |
https://doi.org/10.1109/JIOT.2019.2905788 |
| DOI |
|
|
関連タイプ |
isVersionOf |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
10.1109/JIOT.2019.2905788 |
| 日本十進分類法 |
|
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主題Scheme |
NDC |
|
主題 |
007 |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2327-4662 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| 著者版フラグ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
|
|
内容記述タイプ |
Other |
|
内容記述 |
application/pdf |
ITJ_6_3_5766_5777 (3.2 MB)
