A Robust Indoor Positioning System Based on the Procrustes Analysis and Weighted Extreme Learning Machine

Zou, Han, Huang, Baoqi, Lu, Xiaoxuan ORCID: 0000-0002-3733-4480, Jiang, Hao and Xie, Lihua (2016) A Robust Indoor Positioning System Based on the Procrustes Analysis and Weighted Extreme Learning Machine. IEEE Transactions on Wireless Communications, 15 (2). pp. 1252-1266.

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Official URL: https://ieeexplore.ieee.org/document/7293674

Abstract

Indoor positioning system (IPS) has become one of the most attractive research fields due to the increasing demands on location-based services (LBSs) in indoor environments. Various IPSs have been developed under different circumstances, and most of them adopt the fingerprinting technique to mitigate pervasive indoor multipath effects. However, the performance of the fingerprinting technique severely suffers from device heterogeneity existing across commercial off-the-shelf mobile devices (e.g., smart phones, tablet computers, etc.) and indoor environmental changes (e.g., the number, distribution and activities of people, the placement of furniture, etc.). In this paper, we transform the received signal strength (RSS) to a standardized location fingerprint based on the Procrustes analysis, and introduce a similarity metric, termed signal tendency index (STI), for matching standardized fingerprints. An analysis of the capability of the proposed STI to handle device heterogeneity and environmental changes is presented. We further develop a robust and precise IPS by integrating the merits of both the STI and weighted extreme learning machine (WELM). Finally, extensive experiments are carried out and a performance comparison with existing solutions verifies the superiority of the proposed IPS in terms of robustness to device heterogeneity.

Item Type:	Article
Uncontrolled Keywords:	Robustness, Mobile handsets, Training, Accuracy, Shape, Databases
Depositing User:	Symplectic Admin
Date Deposited:	29 May 2020 10:40
Last Modified:	01 May 2023 17:59
DOI:	10.1109/twc.2015.2487963
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3089158