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
License
File Access Level
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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Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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