Enhancing sparse fingerprint using signal interpolation for indoor positioning

Fingerprinting technology used for localization can be extensively applied in both indoor and outdoor settings, playing a role in scenarios such as autonomous driving and robotic cruising. However, this method is costly due to the high expenses associated with data collection and the substantial com...

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Bibliographic Details
Main Author: He, Qianyu
Other Authors: Chau Yuen
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2025
Subjects:
Online Access:https://hdl.handle.net/10356/182143
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author He, Qianyu
author2 Chau Yuen
author_facet Chau Yuen
He, Qianyu
author_sort He, Qianyu
collection NTU
description Fingerprinting technology used for localization can be extensively applied in both indoor and outdoor settings, playing a role in scenarios such as autonomous driving and robotic cruising. However, this method is costly due to the high expenses associated with data collection and the substantial computational power required for processing. This project involves utilizing the well-established UJIIndoorLoc dataset, performing data cleaning and sampling, and enhancing the dataset using Kriging and Inverse Distance Weighting (IDW) interpolation methods. The performances of these methods in positioning scenarios are then compared. Finally, the potential and efficiency of further enhancing the fingerprinting process using machine learning models will be discussed.
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spelling ntu-10356/1821432025-01-10T15:47:23Z Enhancing sparse fingerprint using signal interpolation for indoor positioning He, Qianyu Chau Yuen School of Electrical and Electronic Engineering chau.yuen@ntu.edu.sg Computer and Information Science Fingerprinting technology used for localization can be extensively applied in both indoor and outdoor settings, playing a role in scenarios such as autonomous driving and robotic cruising. However, this method is costly due to the high expenses associated with data collection and the substantial computational power required for processing. This project involves utilizing the well-established UJIIndoorLoc dataset, performing data cleaning and sampling, and enhancing the dataset using Kriging and Inverse Distance Weighting (IDW) interpolation methods. The performances of these methods in positioning scenarios are then compared. Finally, the potential and efficiency of further enhancing the fingerprinting process using machine learning models will be discussed. Master's degree 2025-01-10T02:45:53Z 2025-01-10T02:45:53Z 2024 Thesis-Master by Coursework He, Q. (2024). Enhancing sparse fingerprint using signal interpolation for indoor positioning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/182143 https://hdl.handle.net/10356/182143 en application/pdf Nanyang Technological University
spellingShingle Computer and Information Science
He, Qianyu
Enhancing sparse fingerprint using signal interpolation for indoor positioning
title Enhancing sparse fingerprint using signal interpolation for indoor positioning
title_full Enhancing sparse fingerprint using signal interpolation for indoor positioning
title_fullStr Enhancing sparse fingerprint using signal interpolation for indoor positioning
title_full_unstemmed Enhancing sparse fingerprint using signal interpolation for indoor positioning
title_short Enhancing sparse fingerprint using signal interpolation for indoor positioning
title_sort enhancing sparse fingerprint using signal interpolation for indoor positioning
topic Computer and Information Science
url https://hdl.handle.net/10356/182143
work_keys_str_mv AT heqianyu enhancingsparsefingerprintusingsignalinterpolationforindoorpositioning