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Mathematical Problems in Engineering
Volume 2015, Article ID 581909, 12 pages
Research Article

A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

1Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science & Engineering, Southeast University, Nanjing 210096, China
2Industrial Center, Nanjing Institute of Technology, Nanjing 211167, China
3Henan University of Technology, Zhengzhou 450007, China

Received 3 July 2014; Revised 13 April 2015; Accepted 15 April 2015

Academic Editor: Filippo Ubertini

Copyright © 2015 Xiaosu 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.


The Kalman filter (KF), which recursively generates a relatively optimal estimate of underlying system state based upon a series of observed measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system model and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless data set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF) algorithm for the integrated navigation system composed of a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), and a magnetic compass (MCP) is proposed. The evolutionary artificial neural networks (EANN) are used in self-learning and training of the intelligent data fusion algorithm. The proposed algorithm can significantly outperform the traditional KF in providing estimation continuously with higher accuracy and smoothing the KF outputs when observation data are inaccurate or unavailable for a short period. The experiments of the prototype verify the effectiveness of the proposed method.