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Symmetry 2017, 9(3), 30; doi:10.3390/sym9030030

Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine

School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
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Academic Editor: Ka Lok Man
Received: 12 November 2016 / Revised: 15 February 2017 / Accepted: 20 February 2017 / Published: 26 February 2017
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Abstract

Nowadays, the increasing demands of location-based services (LBS) have spurred the rapid development of indoor positioning systems (IPS). However, the performance of IPSs is affected by the fluctuation of the measured signal. In this study, a Gaussian filtering algorithm based on an extreme learning machine (ELM) is proposed to address the problem of inaccurate indoor positioning when significant Received Signal Strength Indication (RSSI) fluctuations happen during the measurement process. The Gaussian filtering method is analyzed and compared, which can effectively filter out the fluctuant signals that were caused by the environment effects in an RFID-based positioning system. Meanwhile, the fast learning ability of the proposed ELM algorithm can reduce the time consumption for the offline and online service, and establishes the network positioning regression model between the signal strengths of the tags and their corresponding positions. The proposed positioning system is tested in a real experimental environment. In addition, system test results demonstrate that the positioning algorithms can not only provide higher positioning accuracy, but also achieve a faster computational efficiency compared with other previous algorithms. View Full-Text
Keywords: indoor positioning system; RFID; extreme learning machine; Gaussian filtering indoor positioning system; RFID; extreme learning machine; Gaussian filtering
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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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Wang, C.; Shi, Z.; Wu, F. Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine. Symmetry 2017, 9, 30.

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