Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/82113
Título: | Ensembling multiple radio maps with dynamic noise in fingerprint-based indoor positioning |
Autor(es): | Torres-Sospedra, Joaquín Aranda, Fernando J. Alvarez, Fernando J. Quezada-Gaibor, Darwin Silva, Ivo Miguel Menezes Pendão, Cristiano Gonçalves Moreira, Adriano |
Palavras-chave: | Indoor Positioning Fingerprinting Radio Map Noisy samples Ensemble |
Data: | 2021 |
Editora: | IEEE |
Revista: | IEEE Vehicular Technology Conference |
Citação: | J. Torres-Sospedra et al., "Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning," 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, 2021, pp. 1-5, doi: 10.1109/VTC2021-Spring51267.2021.9448947 |
Resumo(s): | Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/82113 |
ISBN: | 9781728189642 |
DOI: | 10.1109/VTC2021-Spring51267.2021.9448947 |
ISSN: | 1550-2252 |
Versão da editora: | https://ieeexplore.ieee.org/document/9448947 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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_VTCSPRING_2021__Ensembling_Position_Estimation_from_Noisy_Radio_Maps.pdf | 1,43 MB | Adobe PDF | Ver/Abrir |