Transfer Learning for Convolutional Indoor Positioning Systems
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Other documents of the author: Klus, Roman; Klus, Lucie; Talvitie, Jukka; Pihlajasalo, Jaakko; Torres-Sospedra, Joaquín; Valkama, Mikko
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Title
Transfer Learning for Convolutional Indoor Positioning SystemsAuthor (s)
Date
2021-11-29Publisher
IEEEBibliographic citation
KLUS, Roman, et al. Transfer Learning for Convolutional Indoor Positioning Systems. En 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2022. p. 1-8.Type
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Abstract
Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network ... [+]
Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions. [-]
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Ponencia presentada en la 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 29 Nov.-2 Dec. 2021, Lloret de Mar (Spain)
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