Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning

Article

Nabati, M., Ghorashi, S. and Shahbazian, R. 2021. Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning. IEEE Communications Letters. 25 (4), pp. 1192-1195. https://doi.org/10.1109/LCOMM.2020.3047352
AuthorsNabati, M., Ghorashi, S. and Shahbazian, R.
Abstract

Fingerprint-based indoor positioning uses pattern
recognition algorithms (PRAs) to estimate the users’ locations in wireless local area network environments, where satellite-based positioning methods cannot work properly. Traditionally, the training phase of PRA is separately conducted for π‘₯ and 𝑦 coordinates. However, the received signal strength from access points is a unique fingerprint for each measured point, not for π‘₯ and 𝑦 coordinates separately. In this letter, we propose a method to jointly employ the π‘₯ and 𝑦 coordinates during the training phase using a novel PRA-based Gaussian process regression (GPR), named 2D-GPR. Experimental results show that the proposed 2D-GPR improves the accuracy of positioning more than 40π‘π‘š in limited data samples and has a lower calculation cost compared with conventional GPR.

Keywordswireless local area network; fingerprint-based positioning; machine learning; pattern recognition
JournalIEEE Communications Letters
Journal citation25 (4), pp. 1192-1195
ISSN1089-7798
1558-2558
Year2021
PublisherIEEE
Accepted author manuscript
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Anyone
Digital Object Identifier (DOI)https://doi.org/10.1109/LCOMM.2020.3047352
Publication dates
Online24 Dec 2020
PrintApr 2021
Publication process dates
AcceptedDec 2020
Deposited30 Mar 2021
Copyright holderΒ© 2020 IEEE
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