A Statistical Analysis of Feature Transformation for Efficient Localisation in Urban Environments

Azad Adil Shareef; Jaspreet Kaur; Muhammad A. Imran; Haithem Taha Mohammed Ali; Qammer H. Abbasi; Hasan T. Abbas

doi:10.1109/USNC-URSI52151.2023.10238286

A Statistical Analysis of Feature Transformation for Efficient Localisation in Urban Environments

Azad Adil Shareef
, Jaspreet Kaur
, Muhammad A. Imran
, Haithem Taha Mohammed Ali
, Qammer H. Abbasi
, Hasan T. Abbas

University of Dohuk
University of Glasgow
University of Zakho

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

1 Scopus citations

Abstract

In this paper, we perform a transformation-based statistical analysis with an eye to designing a robust and efficient localisation scheme. To this end, we evaluate the coefficient of determination (COD) also denoted as R2 on simulated electromagnetic wave propagation models in an urban environment. Our transformation-based statistical models show that two measurable network parameters, namely the received power (RP) and the time of arrival (ToA), present a strong correlation with a mobile user's given location. By transforming the network parameters we were able to achieve COD of 0.577 for the RP and 0.549 for ToA using the Modulus transformation. We believe that by exploiting the high correlation of parameter transformation, there is potential to design fast and robust machine learning localisation schemes through which the future location of a mobile user can be accurately and reliably predicted.

Original language	English
Title of host publication	2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	269-270
Number of pages	2
ISBN (Electronic)	9781665442282
DOIs	https://doi.org/10.1109/USNC-URSI52151.2023.10238286
State	Published - 2023
Externally published	Yes
Event	2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Portland, United States Duration: 23 Jul 2023 → 28 Jul 2023

Publication series

Name	IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)
Volume	2023-July
ISSN (Print)	1522-3965

Conference

Conference	2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023
Country/Territory	United States
City	Portland
Period	23/07/23 → 28/07/23

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1109/USNC-URSI52151.2023.10238286

Cite this

Shareef, A. A., Kaur, J., Imran, M. A., Ali, H. T. M., Abbasi, Q. H., & Abbas, H. T. (2023). A Statistical Analysis of Feature Transformation for Efficient Localisation in Urban Environments. In 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Proceedings (pp. 269-270). (IEEE Antennas and Propagation Society, AP-S International Symposium (Digest); Vol. 2023-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/USNC-URSI52151.2023.10238286

Shareef, Azad Adil ; Kaur, Jaspreet ; Imran, Muhammad A. et al. / A Statistical Analysis of Feature Transformation for Efficient Localisation in Urban Environments. 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 269-270 (IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)).

@inproceedings{bd764b2e7a984ebd9b040fc139e9f3da,

title = "A Statistical Analysis of Feature Transformation for Efficient Localisation in Urban Environments",

abstract = "In this paper, we perform a transformation-based statistical analysis with an eye to designing a robust and efficient localisation scheme. To this end, we evaluate the coefficient of determination (COD) also denoted as R2 on simulated electromagnetic wave propagation models in an urban environment. Our transformation-based statistical models show that two measurable network parameters, namely the received power (RP) and the time of arrival (ToA), present a strong correlation with a mobile user's given location. By transforming the network parameters we were able to achieve COD of 0.577 for the RP and 0.549 for ToA using the Modulus transformation. We believe that by exploiting the high correlation of parameter transformation, there is potential to design fast and robust machine learning localisation schemes through which the future location of a mobile user can be accurately and reliably predicted.",

author = "Shareef, \{Azad Adil\} and Jaspreet Kaur and Imran, \{Muhammad A.\} and Ali, \{Haithem Taha Mohammed\} and Abbasi, \{Qammer H.\} and Abbas, \{Hasan T.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 ; Conference date: 23-07-2023 Through 28-07-2023",

year = "2023",

doi = "10.1109/USNC-URSI52151.2023.10238286",

language = "English",

series = "IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)",

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

pages = "269--270",

booktitle = "2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Proceedings",

address = "United States",

}

Shareef, AA, Kaur, J, Imran, MA, Ali, HTM, Abbasi, QH & Abbas, HT 2023, A Statistical Analysis of Feature Transformation for Efficient Localisation in Urban Environments. in 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Proceedings. IEEE Antennas and Propagation Society, AP-S International Symposium (Digest), vol. 2023-July, Institute of Electrical and Electronics Engineers Inc., pp. 269-270, 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023, Portland, United States, 23/07/23. https://doi.org/10.1109/USNC-URSI52151.2023.10238286

A Statistical Analysis of Feature Transformation for Efficient Localisation in Urban Environments. / Shareef, Azad Adil; Kaur, Jaspreet; Imran, Muhammad A. et al.
2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 269-270 (IEEE Antennas and Propagation Society, AP-S International Symposium (Digest); Vol. 2023-July).

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

TY - GEN

T1 - A Statistical Analysis of Feature Transformation for Efficient Localisation in Urban Environments

AU - Shareef, Azad Adil

AU - Kaur, Jaspreet

AU - Imran, Muhammad A.

AU - Ali, Haithem Taha Mohammed

AU - Abbasi, Qammer H.

AU - Abbas, Hasan T.

PY - 2023

Y1 - 2023

N2 - In this paper, we perform a transformation-based statistical analysis with an eye to designing a robust and efficient localisation scheme. To this end, we evaluate the coefficient of determination (COD) also denoted as R2 on simulated electromagnetic wave propagation models in an urban environment. Our transformation-based statistical models show that two measurable network parameters, namely the received power (RP) and the time of arrival (ToA), present a strong correlation with a mobile user's given location. By transforming the network parameters we were able to achieve COD of 0.577 for the RP and 0.549 for ToA using the Modulus transformation. We believe that by exploiting the high correlation of parameter transformation, there is potential to design fast and robust machine learning localisation schemes through which the future location of a mobile user can be accurately and reliably predicted.

AB - In this paper, we perform a transformation-based statistical analysis with an eye to designing a robust and efficient localisation scheme. To this end, we evaluate the coefficient of determination (COD) also denoted as R2 on simulated electromagnetic wave propagation models in an urban environment. Our transformation-based statistical models show that two measurable network parameters, namely the received power (RP) and the time of arrival (ToA), present a strong correlation with a mobile user's given location. By transforming the network parameters we were able to achieve COD of 0.577 for the RP and 0.549 for ToA using the Modulus transformation. We believe that by exploiting the high correlation of parameter transformation, there is potential to design fast and robust machine learning localisation schemes through which the future location of a mobile user can be accurately and reliably predicted.

UR - https://www.scopus.com/pages/publications/85172422439

U2 - 10.1109/USNC-URSI52151.2023.10238286

DO - 10.1109/USNC-URSI52151.2023.10238286

M3 - Conference contribution

AN - SCOPUS:85172422439

T3 - IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)

SP - 269

EP - 270

BT - 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

Y2 - 23 July 2023 through 28 July 2023

ER -

Shareef AA, Kaur J, Imran MA, Ali HTM, Abbasi QH, Abbas HT. A Statistical Analysis of Feature Transformation for Efficient Localisation in Urban Environments. In 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 269-270. (IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)). doi: 10.1109/USNC-URSI52151.2023.10238286

A Statistical Analysis of Feature Transformation for Efficient Localisation in Urban Environments

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this