TY - GEN
T1 - Nonlinear initial alignment of strapdown inertial navigation system using CSVM
AU - Wang, He Nian
AU - Yi, Guo Xing
AU - Wang, Chang Hong
AU - Guan, Yu
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Combination support vector machine (CSVM)
KW - Information fusion
KW - Initial alignment
KW - Unscented Kalman filter(UKF)
UR - https://www.scopus.com/pages/publications/84862921446
U2 - 10.4028/www.scientific.net/AMM.148-149.616
DO - 10.4028/www.scientific.net/AMM.148-149.616
M3 - 会议稿件
AN - SCOPUS:84862921446
SN - 9783037853405
T3 - Applied Mechanics and Materials
SP - 616
EP - 620
BT - Mechanical Engineering, Materials and Energy
T2 - 2011 International Conference on Mechanical Engineering, Materials and Energy, ICMEME 2011
Y2 - 19 October 2011 through 21 October 2011
ER -