Performance Enhancement of Low-Cost INS/GPS Navigation System Operating in Urban Environments

Yunus Emre Ozdemir; Oguz Kagan Isik; Patrick Geragersian; Ivan Petrunin; Raphael Grech; Ronald Wong

doi:10.2514/6.2023-2241

Performance Enhancement of Low-Cost INS/GPS Navigation System Operating in Urban Environments

Yunus Emre Ozdemir
, Oguz Kagan Isik
, Patrick Geragersian
, Ivan Petrunin
, Raphael Grech
, Ronald Wong

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

2 Citations (Scopus)

Abstract

As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals.

Original language	English
Title of host publication	AIAA SciTech Forum and Exposition, 2023
Publisher	American Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)	9781624106996
DOIs	https://doi.org/10.2514/6.2023-2241
Publication status	Published - 2023
Externally published	Yes
Event	AIAA SciTech Forum and Exposition, 2023 - Orlando, United States Duration: 23 Jan 2023 → 27 Jan 2023

Publication series

Name	AIAA SciTech Forum and Exposition, 2023

Conference

Conference	AIAA SciTech Forum and Exposition, 2023
Country/Territory	United States
City	Orlando
Period	23/01/23 → 27/01/23

Bibliographical note

Publisher Copyright:
© 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Access to Document

10.2514/6.2023-2241

Cite this

@inproceedings{9ff0badf4f0b4c5ea240e591caca7b05,

title = "Performance Enhancement of Low-Cost INS/GPS Navigation System Operating in Urban Environments",

abstract = "As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2\% NLoS/MP detection accuracy was obtained, and 29.8\% accuracy enhancement was achieved by excluding these detected signals.",

author = "Ozdemir, \{Yunus Emre\} and Isik, \{Oguz Kagan\} and Patrick Geragersian and Ivan Petrunin and Raphael Grech and Ronald Wong",

note = "Publisher Copyright: {\textcopyright} 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.; AIAA SciTech Forum and Exposition, 2023 ; Conference date: 23-01-2023 Through 27-01-2023",

year = "2023",

doi = "10.2514/6.2023-2241",

language = "English",

isbn = "9781624106996",

series = "AIAA SciTech Forum and Exposition, 2023",

publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",

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}

Ozdemir, YE, Isik, OK, Geragersian, P, Petrunin, I, Grech, R & Wong, R 2023, Performance Enhancement of Low-Cost INS/GPS Navigation System Operating in Urban Environments. in AIAA SciTech Forum and Exposition, 2023. AIAA SciTech Forum and Exposition, 2023, American Institute of Aeronautics and Astronautics Inc, AIAA, AIAA SciTech Forum and Exposition, 2023, Orlando, United States, 23/01/23. https://doi.org/10.2514/6.2023-2241

Performance Enhancement of Low-Cost INS/GPS Navigation System Operating in Urban Environments. / Ozdemir, Yunus Emre; Isik, Oguz Kagan; Geragersian, Patrick et al.
AIAA SciTech Forum and Exposition, 2023. American Institute of Aeronautics and Astronautics Inc, AIAA, 2023. (AIAA SciTech Forum and Exposition, 2023).

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

TY - GEN

T1 - Performance Enhancement of Low-Cost INS/GPS Navigation System Operating in Urban Environments

AU - Ozdemir, Yunus Emre

AU - Isik, Oguz Kagan

AU - Geragersian, Patrick

AU - Petrunin, Ivan

AU - Grech, Raphael

AU - Wong, Ronald

PY - 2023

Y1 - 2023

N2 - As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals.

AB - As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals.

UR - https://www.scopus.com/pages/publications/85196726080

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DO - 10.2514/6.2023-2241

M3 - Conference contribution

AN - SCOPUS:85196726080

SN - 9781624106996

T3 - AIAA SciTech Forum and Exposition, 2023

BT - AIAA SciTech Forum and Exposition, 2023

PB - American Institute of Aeronautics and Astronautics Inc, AIAA

T2 - AIAA SciTech Forum and Exposition, 2023

Y2 - 23 January 2023 through 27 January 2023

ER -

Performance Enhancement of Low-Cost INS/GPS Navigation System Operating in Urban Environments

Abstract

Publication series

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Bibliographical note

UN SDGs

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