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Open AccessFeature PaperArticle

Improved RSSI-Based Data Augmentation Technique for Fingerprint Indoor Localisation

Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea
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Electronics 2020, 9(5), 851; https://doi.org/10.3390/electronics9050851
Received: 4 April 2020 / Revised: 15 May 2020 / Accepted: 19 May 2020 / Published: 21 May 2020
(This article belongs to the Special Issue Innovative Technologies in Telecommunication)
Recently, deep-learning-based indoor localisation systems have attracted attention owing to their higher performance compared with traditional indoor localization systems. However, to achieve satisfactory performance, the former systems require large amounts of data to train deep learning models. Since obtaining the data is usually a tedious task, this requirement deters the use of deep learning approaches. To address this problem, we propose an improved data augmentation technique based on received signal strength indication (RSSI) values for fingerprint indoor positioning systems. The technique is implemented using available RSSI values at one reference point, and unlike existing techniques, it mimics the constantly varying RSSI signals. With this technique, the proposed method achieves a test accuracy of 95.26% in the laboratory simulation and 94.59% in a real-time environment, and the average location error is as low as 1.45 and 1.60 m, respectively. The method exhibits higher performance compared with an existing augmentation method. In particular, the data augmentation technique can be applied irrespective of the positioning algorithm used. View Full-Text
Keywords: RSSI augmentation; CNN; indoor positioning; fingerprint RSSI augmentation; CNN; indoor positioning; fingerprint
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Sinha, R.S.; Hwang, S.-H. Improved RSSI-Based Data Augmentation Technique for Fingerprint Indoor Localisation. Electronics 2020, 9, 851.

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