Dual-modal Indoor Mobile Localization System based on Prediction Algorithm

摘要：

Object localization defines an important application for wireless sensor networks. In this paper, we present a hybrid of dual-modal mobile localization system to support the object tracking in indoor environment. In order to decrease the system cost and simplify the sensor deployment, we implement the localization by the received radio signal strength approach and the unscented Kalman filter (SPKF) algorithm in active and passive dual-modal architecture. We realize the system by employing the wireless sensor network and the LAN medium Zigbee/802.15.4. Experimental results demonstrate that the hybrid mobile localization system can significantly improve the localization accuracy and robustness, and reduce the cost of communication among sensor nodes while mobile user is moving in the indoor environments.

关键词： mobile localization system dual-modal Unscented Kalman Filter

作者: Lujia Wang Chao Hu Jinkuan Wang Longqiang Tian Max Q.-H. Meng

作者单位: Shenzhen Institute of Advance Technology Chinese Academy of Sciences School of information Science & Engineering Northeastern University Department of Electronic Engineering The Chinese University of Hong Kong

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)

会议地点: 珠海、澳门

会议语种:英文

页码: 236-241

在线出版日期: 2009-06-22（万方平台首次上网日期，不代表论文的发表时间）