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Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application

Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea
JMP Systems Co., Ltd, Gyeonggi-do 12930, Korea
Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 554;
Received: 25 April 2019 / Revised: 11 May 2019 / Accepted: 13 May 2019 / Published: 17 May 2019
(This article belongs to the Special Issue Indoor Positioning Techniques)
PDF [5053 KB, uploaded 17 May 2019]


In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days’ worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices. View Full-Text
Keywords: augmentation; deep learning; CNN; indoor positioning; fingerprint augmentation; deep learning; CNN; indoor positioning; fingerprint

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Sinha, R.S.; Lee, S.-M.; Rim, M.; Hwang, S.-H. Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application. Electronics 2019, 8, 554.

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