DOA-based localization algorithms under NLOS conditions

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

doi:10.1109/LISAT.2018.8378027

DOA-based localization algorithms under NLOS conditions

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

Electrical and Computer Engineering

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

Abstract

Localization schemes based on direction of arrival (DOA) in none-line-of-sight (NLOS) environments are developed. The proposed kernel-based machine learning method is innovative and can provide accurate position estimation under none-line-of sight (NLOS) conditions. The proposed kernel-based method is compared with the Weighted K-nearest neighborhood (WKNN) fingerprinting method using simulated DOA data in practical rural environment. It shows that the kernel-based method gives more accurate localization results.

Original language	English (US)
Title of host publication	2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538650295
DOIs	https://doi.org/10.1109/LISAT.2018.8378027
State	Published - Jun 8 2018
Event	2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018 - Farmingdale, United States Duration: May 4 2018 → …

Publication series

Name	2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018

Other

Other	2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018
Country/Territory	United States
City	Farmingdale
Period	5/4/18 → …

Keywords

AOA
DOA
Kernel-based Machine Learning
Localization
NLOS
fingerprinting

ASJC Scopus subject areas

Computer Science Applications
Software
Safety, Risk, Reliability and Quality
Computer Networks and Communications
Information Systems and Management
Education
Health(social science)

Access to Document

10.1109/LISAT.2018.8378027

Cite this

Li, J., Lu, I. T., & Lu, J. S. (2018). DOA-based localization algorithms under NLOS conditions. In 2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018 (pp. 1-6). (2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LISAT.2018.8378027

DOA-based localization algorithms under NLOS conditions. / Li, Jun; Lu, I. Tai; Lu, Jonathan S.
2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6 (2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018).

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

Li, J, Lu, IT & Lu, JS 2018, DOA-based localization algorithms under NLOS conditions. in 2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018. 2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018, Farmingdale, United States, 5/4/18. https://doi.org/10.1109/LISAT.2018.8378027

@inproceedings{4074fcf203c349619ff7a712c12931c9,

title = "DOA-based localization algorithms under NLOS conditions",

abstract = "Localization schemes based on direction of arrival (DOA) in none-line-of-sight (NLOS) environments are developed. The proposed kernel-based machine learning method is innovative and can provide accurate position estimation under none-line-of sight (NLOS) conditions. The proposed kernel-based method is compared with the Weighted K-nearest neighborhood (WKNN) fingerprinting method using simulated DOA data in practical rural environment. It shows that the kernel-based method gives more accurate localization results.",

keywords = "AOA, DOA, Kernel-based Machine Learning, Localization, NLOS, fingerprinting",

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

year = "2018",

month = jun,

day = "8",

doi = "10.1109/LISAT.2018.8378027",

language = "English (US)",

series = "2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018",

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

pages = "1--6",

booktitle = "2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018",

note = "2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018 ; Conference date: 04-05-2018",

}

TY - GEN

T1 - DOA-based localization algorithms under NLOS conditions

AU - Li, Jun

AU - Lu, I. Tai

AU - Lu, Jonathan S.

PY - 2018/6/8

Y1 - 2018/6/8

N2 - Localization schemes based on direction of arrival (DOA) in none-line-of-sight (NLOS) environments are developed. The proposed kernel-based machine learning method is innovative and can provide accurate position estimation under none-line-of sight (NLOS) conditions. The proposed kernel-based method is compared with the Weighted K-nearest neighborhood (WKNN) fingerprinting method using simulated DOA data in practical rural environment. It shows that the kernel-based method gives more accurate localization results.

AB - Localization schemes based on direction of arrival (DOA) in none-line-of-sight (NLOS) environments are developed. The proposed kernel-based machine learning method is innovative and can provide accurate position estimation under none-line-of sight (NLOS) conditions. The proposed kernel-based method is compared with the Weighted K-nearest neighborhood (WKNN) fingerprinting method using simulated DOA data in practical rural environment. It shows that the kernel-based method gives more accurate localization results.

KW - AOA

KW - DOA

KW - Kernel-based Machine Learning

KW - Localization

KW - NLOS

KW - fingerprinting

UR - http://www.scopus.com/inward/record.url?scp=85049886768&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049886768&partnerID=8YFLogxK

U2 - 10.1109/LISAT.2018.8378027

DO - 10.1109/LISAT.2018.8378027

M3 - Conference contribution

AN - SCOPUS:85049886768

T3 - 2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018

SP - 1

EP - 6

BT - 2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018

Y2 - 4 May 2018

ER -

DOA-based localization algorithms under NLOS conditions

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this