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The Scientific World Journal
Volume 2014 (2014), Article ID 138548, 7 pages
Research Article

Adaptive Iterated Extended Kalman Filter and Its Application to Autonomous Integrated Navigation for Indoor Robot

Yuan Xu,1,2 Xiyuan Chen,1,2 and Qinghua Li1,3

1School of Instrument Science and Engineering, Southeast University, Nanjing, China
2Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing, China
3School of Electrical Engineering and Automation, Qilu University of Technology, Jinan, China

Received 24 October 2013; Accepted 30 December 2013; Published 13 February 2014

Academic Editors: S. Balochian, V. Bhatnagar, and Y. Zhang

Copyright © 2014 Yuan Xu 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 core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.