DocumentCode :
3582884
Title :
Application of Sage-Husa adaptive filtering algorithm for high precision SINS initial alignment
Author :
Su Wan-xin
Author_Institution :
Fine Mech. & Phys., Changchun Inst. of Opt., Changchun, China
fYear :
2014
Firstpage :
359
Lastpage :
364
Abstract :
When the system model and noise statistical characteristics are known, the conventional Kalman filtering algorithm is suitable. In most cases, the noise statistics are unknown. To improve the alignment precision and convergence speed of strap-down inertial navigation system, an initial alignment method based on Sage-Husa adaptive filter is proposed. Automatic on-line estimation and correction for the noise parameters, the state of the system and the state estimate covariance by the observed data. Using forgetting factor can limit memory length of the filter, which could enhance the effect the newly observed data acts on the present estimation. Thus, enable the system to achieve the best filtering effect. Through simulation verifiable, the adaptive Kalman filter algorithm, improve the convergence speed and alignment accuracy effectively.
Keywords :
adaptive Kalman filters; covariance analysis; estimation theory; inertial navigation; Sage-Husa adaptive filtering; adaptive Kalman filter; alignment precision; automatic on-line estimation; convergence speed; forgetting factor; high precision SINS initial alignment; noise parameters; noise statistical characteristics; state estimate covariance; strap-down inertial navigation system; Accuracy; Convergence; Estimation; Filtering algorithms; Kalman filters; Mathematical model; Noise; Kalman; SINS; Sage-Husa; filter; initial alignment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2014 11th International Computer Conference on
Print_ISBN :
978-1-4799-7207-4
Type :
conf
DOI :
10.1109/ICCWAMTIP.2014.7073426
Filename :
7073426
Link To Document :
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