Kernel Principal Component Analysis-Based Method for Fault Diagnosis of SINS

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 214)

Abstract

Fault diagnosis is necessary in inertial navigation system to ensure navigation success. The kernel principal component analysis (KPCA) method is applied to fault diagnosis of inertial navigation system. At first, the square prediction error is used as the fault monitoring indictor. In addition, this paper locates the faults by using sensor variables changes. In order to reduce the dependence to the prior knowledge in selecting kernel function parameter, genetic algorithm is introduced into the Gauss RBF kernel function parameter optimization. It can improve the scientific nature of parameter choice. Experimental results show that the method based on KPCA of the inertial navigation system has good fault monitoring and recognition ability.

Keywords

Strap down inertial navigation system Sensor Fault diagnosis Kernel principal component analysis Genetic algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.The 301 Teaching and Research SectionThe Second Artillery Engineering UniversityXi’anPeople’s Republic of China

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