Next Article in Journal
A Blockchain and Machine Learning-Based Drug Supply Chain Management and Recommendation System for Smart Pharmaceutical Industry
Next Article in Special Issue
Joint Scheduling and Power Allocation Using Non-Orthogonal Multiple Access in Multi-Cell Beamforming Networks
Previous Article in Journal
ASFIT: AUTOSAR-Based Software Fault Injection Test for Vehicles
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
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 851;
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
Show Figures

Figure 1

MDPI and ACS Style

Sinha, R.S.; Hwang, S.-H. Improved RSSI-Based Data Augmentation Technique for Fingerprint Indoor Localisation. Electronics 2020, 9, 851.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop