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Sensors 2015, 15(1), 1804-1824;

A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine

School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
This paper is an extended version of the paper entitled “An Online Sequential Extreme Learning Machine Approach to WiFi Based Indoor Positioning”, presented at 2014 IEEE World Forum on Internet of Things (WF-IoT), Seoul, Korea, 6–8 March 2014.
Author to whom correspondence should be addressed.
Received: 20 November 2014 / Accepted: 8 January 2015 / Published: 15 January 2015
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
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Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics. View Full-Text
Keywords: indoor localization; online sequential extreme learning machine; WiFi indoor localization; online sequential extreme learning machine; WiFi
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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Zou, H.; Lu, X.; Jiang, H.; Xie, L. A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine. Sensors 2015, 15, 1804-1824.

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