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Mathematical Problems in Engineering
Volume 2012, Article ID 707326, 19 pages
http://dx.doi.org/10.1155/2012/707326
Research Article

INS/WSN-Integrated Navigation Utilizing LS-SVM and 𝐻 Filtering

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

1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
2Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China
3School of Electrical Engineering and Automation, Shandong Polytechnic University, Jinan 250353, China

Received 6 October 2011; Accepted 30 November 2011

Academic Editor: Weihai Zhang

Copyright © 2012 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.

Abstract

In order to achieve continuous navigation capability in areas such as tunnels, urban canyons, and indoors a new approach using least squares support vector machine (LS-SVM) and 𝐻 filter (HF) for integration of INS/WSN is proposed. In the integrated system, HF estimates the errors of position and velocity while the signals in WSNs are available. Meanwhile, the compensation model is trained by LS-SVM with corresponding HF states. Once outages of the signals in WSNs, the model is used to correct INS solution as HF does. Moreover, due to device reasons, there are slight fluctuations in sampling period in practice. For overcoming this problem of integrated navigation, the theoretical analysis and implementation of HF for an integrated navigation system with stochastic uncertainty are also given. Simulation shows the performance of HF is more robust compared with INS-only solution and Kalman filter (KF) solution, and the prediction of LS-SVM has the smallest error compared with INS-only and back propagation (BP), the improvement is particularly obvious.