Paper Title:
Indoor Positioning in WLAN Environment Based on Support Vector Regression and Space Partitioning
  Abstract

Indoor positioning system in wireless local area network (WLAN) has been a subject of intensive research due to its cost effectiveness and reasonable positioning accuracy. A new WLAN indoor positioning algorithm based on support vector regression (SVR) and space partitioning is proposed. The whole positioning environment is partitioned into several subspaces by combining k-means clustering method and binary support vector classifiers (SVC). Then the mapping function between received signal strength (RSS) and the physical space is established by SVR machine for each subspace. Subspace with much smaller physical range means more compact input feature space and leads to the enhancement of generalization capability for each SVR machine. The proposed algorithm and other well-known positioning algorithms are carried and compared in a real WLAN environment. Experimental results show that the proposed algorithm achieves 14.6 percent (0.31m) improvement than the single SVR algorithm in the sense of mean positioning error.

  Info
Periodical
Advanced Materials Research (Volumes 204-210)
Edited by
Helen Zhang, Gang Shen and David Jin
Pages
1599-1602
DOI
10.4028/www.scientific.net/AMR.204-210.1599
Citation
Z. A. Deng, Y. B. Xu, D. Wu, "Indoor Positioning in WLAN Environment Based on Support Vector Regression and Space Partitioning", Advanced Materials Research, Vols. 204-210, pp. 1599-1602, 2011
Online since
February 2011
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Price
$32.00
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