Robust kernel-based machine learning localization using NLOS TOAs or TDOAs

Jun Li; I. Tai Lu; Jonathan S. Lu; Lingwen Zhang

doi:10.1109/LISAT.2017.8001981

Robust kernel-based machine learning localization using NLOS TOAs or TDOAs

Jun Li, I. Tai Lu, Jonathan S. Lu, Lingwen Zhang

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A robust kernel-based machine learning localization scheme using time of arrival (TOA) or time difference of arrival (TDOA) in none-line-of-sight (NLOS) environments is proposed. The scheme can provide accurate position estimation while the reference nodes are coarsely and randomly distributed in the area of interests. Moreover, the scheme is insensitive with respect to random TOA synchronization and measurement errors.

Original language	English (US)
Title of host publication	2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538638873
DOIs	https://doi.org/10.1109/LISAT.2017.8001981
State	Published - Aug 3 2017
Event	2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017 - Farmingdale, United States Duration: May 5 2017 → …

Publication series

Name	2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017

Other

Other	2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017
Country/Territory	United States
City	Farmingdale
Period	5/5/17 → …

Keywords

Kernel-based Machine Learning
Localization
NLOS
TDOA
TOA
fingerprinting

ASJC Scopus subject areas

Artificial Intelligence
Instrumentation
Software
Computer Science Applications
Renewable Energy, Sustainability and the Environment

Access to Document

10.1109/LISAT.2017.8001981

Cite this

Li, J., Lu, I. T., Lu, J. S., & Zhang, L. (2017). Robust kernel-based machine learning localization using NLOS TOAs or TDOAs. In 2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017 Article 8001981 (2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LISAT.2017.8001981

Robust kernel-based machine learning localization using NLOS TOAs or TDOAs. / Li, Jun; Lu, I. Tai; Lu, Jonathan S. et al.
2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8001981 (2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, J, Lu, IT, Lu, JS & Zhang, L 2017, Robust kernel-based machine learning localization using NLOS TOAs or TDOAs. in 2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017., 8001981, 2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017, Farmingdale, United States, 5/5/17. https://doi.org/10.1109/LISAT.2017.8001981

Li J, Lu IT, Lu JS, Zhang L. Robust kernel-based machine learning localization using NLOS TOAs or TDOAs. In 2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8001981. (2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017). doi: 10.1109/LISAT.2017.8001981

@inproceedings{fec24e5cd7d94bd8a1c952f3d4ef8da2,

title = "Robust kernel-based machine learning localization using NLOS TOAs or TDOAs",

abstract = "A robust kernel-based machine learning localization scheme using time of arrival (TOA) or time difference of arrival (TDOA) in none-line-of-sight (NLOS) environments is proposed. The scheme can provide accurate position estimation while the reference nodes are coarsely and randomly distributed in the area of interests. Moreover, the scheme is insensitive with respect to random TOA synchronization and measurement errors.",

keywords = "Kernel-based Machine Learning, Localization, NLOS, TDOA, TOA, fingerprinting",

author = "Jun Li and Lu, {I. Tai} and Lu, {Jonathan S.} and Lingwen Zhang",

year = "2017",

month = aug,

day = "3",

doi = "10.1109/LISAT.2017.8001981",

language = "English (US)",

series = "2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017",

note = "2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017 ; Conference date: 05-05-2017",

}

TY - GEN

T1 - Robust kernel-based machine learning localization using NLOS TOAs or TDOAs

AU - Li, Jun

AU - Lu, I. Tai

AU - Lu, Jonathan S.

AU - Zhang, Lingwen

PY - 2017/8/3

Y1 - 2017/8/3

N2 - A robust kernel-based machine learning localization scheme using time of arrival (TOA) or time difference of arrival (TDOA) in none-line-of-sight (NLOS) environments is proposed. The scheme can provide accurate position estimation while the reference nodes are coarsely and randomly distributed in the area of interests. Moreover, the scheme is insensitive with respect to random TOA synchronization and measurement errors.

AB - A robust kernel-based machine learning localization scheme using time of arrival (TOA) or time difference of arrival (TDOA) in none-line-of-sight (NLOS) environments is proposed. The scheme can provide accurate position estimation while the reference nodes are coarsely and randomly distributed in the area of interests. Moreover, the scheme is insensitive with respect to random TOA synchronization and measurement errors.

KW - Kernel-based Machine Learning

KW - Localization

KW - NLOS

KW - TDOA

KW - TOA

KW - fingerprinting

UR - http://www.scopus.com/inward/record.url?scp=85028618636&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028618636&partnerID=8YFLogxK

U2 - 10.1109/LISAT.2017.8001981

DO - 10.1109/LISAT.2017.8001981

M3 - Conference contribution

AN - SCOPUS:85028618636

T3 - 2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017

BT - 2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017

Y2 - 5 May 2017

ER -

Robust kernel-based machine learning localization using NLOS TOAs or TDOAs

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this