Please use this identifier to cite or link to this item: https://doi.org/10.1145/2185677.2185697
Title: Extreme learning machine for wireless indoor localization
Authors: Xiao, W.
Liu, P.
Soh, W.-S. 
Jin, Y.
Keywords: ELM
Fingerprinting
Indoor localization
Neural network
Issue Date: 2012
Source: Xiao, W.,Liu, P.,Soh, W.-S.,Jin, Y. (2012). Extreme learning machine for wireless indoor localization. IPSN'12 - Proceedings of the 11th International Conference on Information Processing in Sensor Networks : 101-102. ScholarBank@NUS Repository. https://doi.org/10.1145/2185677.2185697
Abstract: Due to the widespread deployment and low cost, WLAN has drawn much attention for indoor localization. In this poster, an efficient indoor localization algorithm, which utilizes the WLAN received signal strength from each Access Point (AP), has been proposed. The algorithm is based on the Extreme Learning Machine (ELM), a Single layer Feed-forward neural Network (SLFN). It is competitive fast in offline learning and online localization. Also, compared with existing fingerprinting approach, it does not need the fingerprinting database in the online phase, which can substantially reduce the required storage space of the terminal devices. © 2012 ACM.
Source Title: IPSN'12 - Proceedings of the 11th International Conference on Information Processing in Sensor Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/70271
ISBN: 9781450312271
DOI: 10.1145/2185677.2185697
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