Nonlinear Initial Alignment of Strapdown Inertial Navigation System Using CSVM

Abstract:

According to the situation that the statistical characteristics of noise in initial alignment of sins of UKF filter is not agree with actually, the filtering precision will severely reduce or even divergent, a combination of support vector machine method of initial alignment is proposed. In this paper, the test samples are split into four groups. Three groups are trained for the first layer and the last group is trained for the second layer of support vector machine. The first layer is a group of support vector machine in parallel computing, the second layer is an information fusion of the single support vector machine in the first layer, and combined support vector machines. In this method initial alignment of strapdown inertial navigation system is achieved. Finally through the UKF filter, SVM, CSVM simulation contrast, the results show that CSVM has an improvement than a single SVM, better real-time than UKF filter and generalization ability.

• Periodical: Applied Mechanics and Materials (Volumes 148-149)
• Pages: 616-620
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.148-149.616
• Online since: December 2011
• Authors: He Nian Wang, Guo Xing Yi, Chang Hong Wang, Yu Guan
• Keywords: Combination Support Vector Machine, Information Fusion, Initial Alignment, Unscented Kalman Filter (UKF)

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