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

Author:

Jiang Xinlong123,Chen Yiqiang12,Liu Junfa12,Liu Dingjun4,Gu Yang123,Chen Zhenyu123

Affiliation:

1. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190, China

2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

3. University of Chinese Academy of Sciences, Beijing 100190, China

4. Xiangtan University, Hunan 411105, China

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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