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Journal of Sensors
Volume 2017 (2017), Article ID 7879198, 11 pages
Research Article

Integrated SINS/WSN Positioning System for Indoor Mobile Target Using Novel Asynchronous Data Fusion Method

1School of Mechatronic Engineering, Southwest Petroleum University, Chengdu, Sichuan 610500, China
2School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
3College of Internet of Things Engineering, Hohai University, Changzhou, Jiangsu 213022, China

Correspondence should be addressed to Wei Li

Received 21 March 2017; Accepted 13 June 2017; Published 20 July 2017

Academic Editor: Mohannad Al-Durgham

Copyright © 2017 Hai Yang 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.


According to the asynchronous transmission of data for the SINS/WSN integrated positioning system, this paper proposes a novel asynchronous data fusion method using Unscented Kalman Filter for SINS/WSN integrated positioning system based on indoor mobile target. The state equation of the integrated system is built with the motion characteristic of mobile target. The pseudo measurement equation is built based on the time sequence of SINS/WSN measured value through detecting the measurement of WSN in every fusion period. Considering that the improved state-space model, comprised of the state equation and pseudo measurement equation, is the nonlinear equations, the Unscented Kalman Filter is applied to estimate the state value of the state-space model. Hence the asynchronous data fusion method for SINS/WSN integrated positioning system can be achieved. Simulation results and experimental tests show that the positioning system with proposed asynchronous data fusion algorithm performs feasibility and stability under circumstances of the asynchronous time, and it is superior to the traditional asynchronous data fusion and synchronous data fusion methods.