Kaur, J. , Shawky, M. , Mollel, M., Popoola, O. , Imran, M. , Abbasi, Q. and Abbas, H. (2024) Indoor Position Estimation Using Machine Learning. [Data Collection]

Collection description

The dataset comprises data recorded from 9 positions within an indoor environment, under both Line of Sight (LOS) and Non-Line of Sight (NLOS) conditions, marked in a square grid and divided into blocks of varying sizes (1 meter, 0.75 meters, and 0.5 meters). Data is organised into 4 text files for each of the 9 positions, capturing amplitude and phase values from 2 receiving antennas, resulting in a total of 36 files. Each file contains 125 x 500 samples, translating to Channel State Information (CSI) matrices combining amplitude and phase information for both channels.

Funding:	Engineering and Physical Sciences Research Council (EPSRC) [PA6130] Engineering and Physical Sciences Research Council (EPSRC) [PB1398]
College / School:	College of Science and Engineering > School of Engineering College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering College of Science and Engineering > School of Engineering > Systems Power and Energy
Date Deposited:	04 Mar 2024 09:25
URI:	https://researchdata.gla.ac.uk/id/eprint/1603
Additional details Additional details Type of data: Spreadsheet, Text Collection period: From To 1 December 2022 1 February 2023 Date: 4 March 2024 Date type: Publication Resource language: English Status: Published Data Publisher: University of Glasgow Copyright holders: University of Glasgow Last Modified: 18 Nov 2024 10:30

Available Files

Data

nlos.zip nlos.zip nlos.zip Visible to: Anyone File size: 19MB License: CC BY 4.0

los.zip los.zip los.zip Visible to: Anyone File size: 21MB License: CC BY 4.0

Documentation

Consent.docx Consent.docx Consent.docx Visible to: Anyone File size: 71kB License: CC BY 4.0

Read me

Readme.docx Readme.docx Readme.docx Visible to: Anyone File size: 762kB License: CC BY 4.0

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