Indoor Position Estimation Using Machine Learning

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.

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

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

University of Glasgow

DOI: 10.5525/gla.researchdata.1603

Retrieved: 2024-04-24

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