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Appl. Sci. 2016, 6(11), 338; doi:10.3390/app6110338

Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion

†,‡,* , †,‡
,
and
Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Current address: No. 2 Chongwen Road, Nan’an District, Chongqing 400000, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Christos Verikoukis
Received: 29 July 2016 / Revised: 31 October 2016 / Accepted: 3 November 2016 / Published: 7 November 2016
View Full-Text   |   Download PDF [1140 KB, uploaded 7 November 2016]   |  

Abstract

Location estimation plays a crucial role in Location-Based Services (LBSs) with satisfactory user experience. The Wireless Local Area Network (WLAN) localization approach is preferred as a cost-efficient solution to indoor localization on account of the widely-deployed WLAN infrastructures. In this paper, we propose a new WLAN Received Signal Strength (RSS)-based indoor localization approach using the semi-supervised manifold alignment with dimension expansion. In concrete terms, we first construct an innovative objective function based on the augmented physical coordinates and the corresponding WLAN RSS measurements. Second, the closed-form solution to the objective function is derived out according to the Lagrange multiplier equation, which results in the manifold in physical coordinate space. Third, the target location is estimated by matching the transformed newly-collected RSS against the manifold. The localization performance with noise perturbation is analyzed upon the constructed objective function, and meanwhile, the closed-form solution to the objective function with respect to multiple types of measurements is also derived out for the sake of leveraging all of the potential measurements for indoor localization. The extensive testing results show that the proposed approach performs well in localization accuracy even at low calibration load, and its performance can be further improved by using multiple types of measurements for localization. View Full-Text
Keywords: WLAN; indoor localization; semi-supervised learning; manifold alignment; dimension expansion WLAN; indoor localization; semi-supervised learning; manifold alignment; dimension expansion
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Zhang, Q.; Zhou, M.; Tian, Z.; Wang, Y. Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion. Appl. Sci. 2016, 6, 338.

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