Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap

Sergio Lembo; Seppo Horsmanheimo; Matti Somersalo; Matti Laukkanen; Lotta Tuomimaki; Sami Huilla

doi:10.1109/IPIN.2019.8911820

Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap

Sergio Lembo, Seppo Horsmanheimo, Matti Somersalo, Matti Laukkanen, Lotta Tuomimaki, Sami Huilla

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

5 Citations (Scopus)

Abstract

We propose a physical arrangement consisting of a 2.4 GHz WiFi radio, and high attenuation surfaces with an air-gap in-between, to delimit a fingerprinting region. We show by simulations, and field measurements, that such arrangement allows to form a characteristic lobe in the radiation pattern after the gap, which is characterized by an abrupt change in the Received Signal Strengths (RSSs) along the direction parallel to the surfaces. From measured RSS samples, we construct, to aid our analysis, an equation that approximates the radiation pattern as a continuous RSS radio map, using symbolic regression. Finally, we observe the positioning performance of this arrangement, performing RSS fingerprint pattern matching with a Neural Network. Results are compared to the positioning accuracy that would be obtained by using an omnidirectional radiation pattern antenna. We conclude that, the abrupt change in RSSs obtained with this arrangement, translates into a better positioning accuracy, close and along a direction parallel to the surfaces.

Original language	English
Title of host publication	2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019
Publisher	IEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)	978-1-7281-1788-1
ISBN (Print)	978-1-7281-1789-8
DOIs	https://doi.org/10.1109/IPIN.2019.8911820
Publication status	Published - Sept 2019
MoE publication type	A4 Article in a conference publication
Event	2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019 - Pisa, Italy Duration: 30 Sept 2019 → 3 Oct 2019

Conference

Conference	2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019
Country/Territory	Italy
City	Pisa
Period	30/09/19 → 3/10/19

Funding

Acknowledgment: This work was funded by Business Finland in LuxTur-rim5G and LuxTurrim5G+ projects.

Keywords

Accuracy
Air-gap
Indoor positioning
Neural Network
Proximity Sensor
Radiation Pattern
RSS fingerprinting
Symbolic Regression
WiFi
OtaNano

Access to Document

10.1109/IPIN.2019.8911820

Cite this

Lembo, S., Horsmanheimo, S., Somersalo, M., Laukkanen, M., Tuomimaki, L., & Huilla, S. (2019). Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap. In 2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019 Article 8911820 IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/IPIN.2019.8911820

@inproceedings{0c302f33b6324d8895486424e19cf4d5,

title = "Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap",

abstract = "We propose a physical arrangement consisting of a 2.4 GHz WiFi radio, and high attenuation surfaces with an air-gap in-between, to delimit a fingerprinting region. We show by simulations, and field measurements, that such arrangement allows to form a characteristic lobe in the radiation pattern after the gap, which is characterized by an abrupt change in the Received Signal Strengths (RSSs) along the direction parallel to the surfaces. From measured RSS samples, we construct, to aid our analysis, an equation that approximates the radiation pattern as a continuous RSS radio map, using symbolic regression. Finally, we observe the positioning performance of this arrangement, performing RSS fingerprint pattern matching with a Neural Network. Results are compared to the positioning accuracy that would be obtained by using an omnidirectional radiation pattern antenna. We conclude that, the abrupt change in RSSs obtained with this arrangement, translates into a better positioning accuracy, close and along a direction parallel to the surfaces.",

keywords = "Accuracy, Air-gap, Indoor positioning, Neural Network, Proximity Sensor, Radiation Pattern, RSS fingerprinting, Symbolic Regression, WiFi, OtaNano",

author = "Sergio Lembo and Seppo Horsmanheimo and Matti Somersalo and Matti Laukkanen and Lotta Tuomimaki and Sami Huilla",

note = "Funding Information: Acknowledgment: This work was funded by Business Finland in LuxTur-rim5G and LuxTurrim5G+ projects. Publisher Copyright: {\textcopyright} 2019 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019 ; Conference date: 30-09-2019 Through 03-10-2019",

year = "2019",

month = sep,

doi = "10.1109/IPIN.2019.8911820",

language = "English",

isbn = "978-1-7281-1789-8",

booktitle = "2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

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}

Lembo, S , Horsmanheimo, S, Somersalo, M, Laukkanen, M , Tuomimaki, L & Huilla, S 2019, Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap. in 2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019., 8911820, IEEE Institute of Electrical and Electronic Engineers, 2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019, Pisa, Italy, 30/09/19. https://doi.org/10.1109/IPIN.2019.8911820

Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap. / Lembo, Sergio ; Horsmanheimo, Seppo; Somersalo, Matti et al.
2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019. IEEE Institute of Electrical and Electronic Engineers, 2019. 8911820.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Enhancing WiFi RSS fingerprint positioning accuracy

T2 - 2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019

AU - Lembo, Sergio

AU - Horsmanheimo, Seppo

AU - Somersalo, Matti

AU - Laukkanen, Matti

AU - Tuomimaki, Lotta

AU - Huilla, Sami

N1 - Funding Information: Acknowledgment: This work was funded by Business Finland in LuxTur-rim5G and LuxTurrim5G+ projects. Publisher Copyright: © 2019 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2019/9

Y1 - 2019/9

N2 - We propose a physical arrangement consisting of a 2.4 GHz WiFi radio, and high attenuation surfaces with an air-gap in-between, to delimit a fingerprinting region. We show by simulations, and field measurements, that such arrangement allows to form a characteristic lobe in the radiation pattern after the gap, which is characterized by an abrupt change in the Received Signal Strengths (RSSs) along the direction parallel to the surfaces. From measured RSS samples, we construct, to aid our analysis, an equation that approximates the radiation pattern as a continuous RSS radio map, using symbolic regression. Finally, we observe the positioning performance of this arrangement, performing RSS fingerprint pattern matching with a Neural Network. Results are compared to the positioning accuracy that would be obtained by using an omnidirectional radiation pattern antenna. We conclude that, the abrupt change in RSSs obtained with this arrangement, translates into a better positioning accuracy, close and along a direction parallel to the surfaces.

AB - We propose a physical arrangement consisting of a 2.4 GHz WiFi radio, and high attenuation surfaces with an air-gap in-between, to delimit a fingerprinting region. We show by simulations, and field measurements, that such arrangement allows to form a characteristic lobe in the radiation pattern after the gap, which is characterized by an abrupt change in the Received Signal Strengths (RSSs) along the direction parallel to the surfaces. From measured RSS samples, we construct, to aid our analysis, an equation that approximates the radiation pattern as a continuous RSS radio map, using symbolic regression. Finally, we observe the positioning performance of this arrangement, performing RSS fingerprint pattern matching with a Neural Network. Results are compared to the positioning accuracy that would be obtained by using an omnidirectional radiation pattern antenna. We conclude that, the abrupt change in RSSs obtained with this arrangement, translates into a better positioning accuracy, close and along a direction parallel to the surfaces.

KW - Accuracy

KW - Air-gap

KW - Indoor positioning

KW - Neural Network

KW - Proximity Sensor

KW - Radiation Pattern

KW - RSS fingerprinting

KW - Symbolic Regression

KW - WiFi

KW - OtaNano

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DO - 10.1109/IPIN.2019.8911820

M3 - Conference article in proceedings

SN - 978-1-7281-1789-8

BT - 2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019

PB - IEEE Institute of Electrical and Electronic Engineers

Y2 - 30 September 2019 through 3 October 2019

ER -

Lembo S , Horsmanheimo S, Somersalo M, Laukkanen M , Tuomimaki L, Huilla S. Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap. In 2019 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2019. IEEE Institute of Electrical and Electronic Engineers. 2019. 8911820 doi: 10.1109/IPIN.2019.8911820

Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap

Abstract

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

OtaNano

Cite this

Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap

Abstract

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Equipment

OtaNano

Cite this