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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 545792, 13 pages
Research Article

Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter

1Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190, China
2Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
3University of Chinese Academy of Sciences, Beijing 100190, China
4Xiangtan University, Hunan 411105, China

Received 24 August 2014; Revised 4 November 2014; Accepted 31 December 2014

Academic Editor: Tao Chen

Copyright © 2015 Xinlong Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As the development of Indoor Location Based Service (Indoor LBS), a timely localization and smooth tracking with high accuracy are desperately needed. Unfortunately, any single method cannot meet the requirement of both high accuracy and real-time ability at the same time. In this paper, we propose a fusion location framework with Particle Filter using Wi-Fi signals and motion sensors. In this framework, we use Extreme Learning Machine (ELM) regression algorithm to predict position based on motion sensors and use Wi-Fi fingerprint location result to solve the error accumulation of motion sensors based location occasionally with Particle Filter. The experiments show that the trajectory is smoother as the real one than the traditional Wi-Fi fingerprint method.