Indoor Position Estimation Using Machine Learning

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nlos.zip

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data archive 32963 disk0/00/00/16/03 2024-03-04 09:25:39 2024-11-18 10:30:11 2024-03-04 09:25:39 data_collection show Kaur Jaspreet 56490 0000-0002-2883-9241 Shawky Mahmoud 66074 0000-0003-3393-8460 Mollel Michael Popoola Olaoluwa 57658 0000-0002-4566-3567 Imran Muhammad 37001 0000-0003-4743-9136 Abbasi Qammer 39488 0000-0002-7097-9969 Abbas Hasan 51264 0000-0002-5951-9603 glaresearchdata:2024-03-04-1603 Indoor Position Estimation Using Machine Learning pub 30300000 30305000 30304000 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. 2024-03-04 published University of Glasgow 10.5525/gla.researchdata.1603 Spreadsheet Text University of Glasgow 320276 CHEDDAR: Communications Hub For Empowering Distributed ClouD Computing Applications And Research CODSE_PA6130 Muhammad Imran Engineering and Physical Sciences Research Council (EPSRC) PA6130 ENG - Autonomous Systems & Connectivity 325518 CHEDDAR Uplift Muhammad Imran Engineering and Physical Sciences Research Council (EPSRC) PB1398 ENG - Autonomous Systems & Connectivity FALSE English 2022-12-01 2023-02-01 2034-03-04 R email TRUE 300200232, 300190109 cose Approving committee: University of Glasgow’s Research Ethics Committee FALSE