DNN-assisted cooperative localization in vehicular networks

Jewon Eom; Hyowon Kim; Sang Hyun Lee; Sunwoo Kim

doi:10.3390/en12142758

DNN-assisted cooperative localization in vehicular networks

Jewon Eom, Hyowon Kim, Sang Hyun Lee, Sunwoo Kim

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

This work develops a deep-learning-based cooperative localization technique for high localization accuracy and real-time operation in vehicular networks. In cooperative localization, the noisy observation of the pairwise distance and the angle between vehicles causes nonlinear optimization problems. To handle such a nonlinear optimization task at each vehicle, a deep neural network (DNN) technique is to replace a cumbersome solution of nonlinear optimization along with the saving of the computational loads. Simulation results demonstrate that the proposed technique attains some performance gain in localization accuracy and computational complexity as compared to existing cooperative localization techniques.

Original language	English
Article number	2758
Journal	Energies
Volume	12
Issue number	14
DOIs	https://doi.org/10.3390/en12142758
Publication status	Published - 2019

Bibliographical note

Funding Information:
This work was partly supported by Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00316, Development of Fundamental Technologies for the Next Generation Public Safety Communications) and Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2016-0-00208, High Accurate Positioning Enabled MIMO Transmission and Network Technologies for Next 5G-V2X (vehicle-to-everything) Services).

Funding Information:
Acknowledgments: This work was partly supported by Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00316, Development of Fundamental Technologies for the Next Generation Public Safety Communications) and Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2016-0-00208, High Accurate Positioning Enabled MIMO Transmission and Network Technologies for Next 5G-V2X (vehicle-to-everything) Services).

Publisher Copyright:
© 2019 by the authors

Keywords

Cooperative localization
Deep neural network
Internet of vehicle
Multilateration
Vehicular networks

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/en12142758

Cite this

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abstract = "This work develops a deep-learning-based cooperative localization technique for high localization accuracy and real-time operation in vehicular networks. In cooperative localization, the noisy observation of the pairwise distance and the angle between vehicles causes nonlinear optimization problems. To handle such a nonlinear optimization task at each vehicle, a deep neural network (DNN) technique is to replace a cumbersome solution of nonlinear optimization along with the saving of the computational loads. Simulation results demonstrate that the proposed technique attains some performance gain in localization accuracy and computational complexity as compared to existing cooperative localization techniques.",

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language = "English",

volume = "12",

journal = "Energies",

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AU - Lee, Sang Hyun

AU - Kim, Sunwoo

N1 - Funding Information: This work was partly supported by Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00316, Development of Fundamental Technologies for the Next Generation Public Safety Communications) and Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2016-0-00208, High Accurate Positioning Enabled MIMO Transmission and Network Technologies for Next 5G-V2X (vehicle-to-everything) Services). Funding Information: Acknowledgments: This work was partly supported by Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00316, Development of Fundamental Technologies for the Next Generation Public Safety Communications) and Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2016-0-00208, High Accurate Positioning Enabled MIMO Transmission and Network Technologies for Next 5G-V2X (vehicle-to-everything) Services). Publisher Copyright: © 2019 by the authors

PY - 2019

Y1 - 2019

N2 - This work develops a deep-learning-based cooperative localization technique for high localization accuracy and real-time operation in vehicular networks. In cooperative localization, the noisy observation of the pairwise distance and the angle between vehicles causes nonlinear optimization problems. To handle such a nonlinear optimization task at each vehicle, a deep neural network (DNN) technique is to replace a cumbersome solution of nonlinear optimization along with the saving of the computational loads. Simulation results demonstrate that the proposed technique attains some performance gain in localization accuracy and computational complexity as compared to existing cooperative localization techniques.

AB - This work develops a deep-learning-based cooperative localization technique for high localization accuracy and real-time operation in vehicular networks. In cooperative localization, the noisy observation of the pairwise distance and the angle between vehicles causes nonlinear optimization problems. To handle such a nonlinear optimization task at each vehicle, a deep neural network (DNN) technique is to replace a cumbersome solution of nonlinear optimization along with the saving of the computational loads. Simulation results demonstrate that the proposed technique attains some performance gain in localization accuracy and computational complexity as compared to existing cooperative localization techniques.

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KW - Deep neural network

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KW - Multilateration

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ER -

DNN-assisted cooperative localization in vehicular networks

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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