Please use this identifier to cite or link to this item:
|Title:||A fast and precise indoor localization algorithm based on an online sequential extreme learning machine||Authors:||Zou, Han
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2015||Source:||Zou, H., Lu, X., Jiang, H., & Xie, L. (2015). A fast and precise indoor localization algorithm based on an online sequential extreme learning machine. Sensors, 15(1), 1804-1824.||Series/Report no.:||Sensors||Abstract:||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.||URI:||https://hdl.handle.net/10356/106642
|ISSN:||1424-8220||DOI:||10.3390/s150101804||Rights:||© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.