Dual-modal indoor mobile localization system based on prediction algorithm

Author

Wang, Lujia ; Hu, Chao ; Wang, Jinkuan ; Tian, Longqiang ; Meng, Max Q -H

Author_Institution

Shenzhen Inst. of Adv. Technol., Chinese Acad. of Sci., Shenzhen, China

fYear

2009

fDate

22-24 June 2009

Firstpage

236

Lastpage

241

Abstract

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.

Keywords

Kalman filters; indoor radio; mobile radio; wireless LAN; wireless sensor networks; 802.15.4 standard; LAN; Zigbee; active dual-modal architecture; dual-modal indoor mobile localization system; indoor environment; mobile user; object localization; object tracking; passive dual-modal architecture; prediction algorithm; received radio signal strength localization; unscented Kalman filter algorithm; wireless sensor networks; Costs; Frequency estimation; Global Positioning System; Indoor environments; Prediction algorithms; Radar tracking; Radio frequency; Radiofrequency identification; Robustness; Ultrasonic imaging; Unscented Kalman Filter; dual-modal; mobile localization system;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation, 2009. ICIA '09. International Conference on

Conference_Location

Zhuhai, Macau

Print_ISBN

978-1-4244-3607-1

Electronic_ISBN

978-1-4244-3608-8

Type

conf

DOI

10.1109/ICINFA.2009.5204928

Filename

5204928