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

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 deplo...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 15; číslo 1; s. 1804 - 1824
Hlavní autoři: Zou, Han, Lu, Xiaoxuan, Jiang, Hao, Xie, Lihua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 15.01.2015
MDPI
Témata:
Accuracy
Algorithms
Costs
Distance learning
Dynamics
Indoor
indoor localization
Internet of Things
Learning
Localization
Manpower
online sequential extreme learning machine
Performance evaluation
Position (location)
Propagation
Receivers & amplifiers
Sensors
Simulation
WiFi
Wireless access points
Singapore
ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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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.
Academic Editor: Kourosh Khoshelham
ISSN:1424-8220
1424-8220
DOI:10.3390/s150101804