Application of Sage-Husa adaptive filtering algorithm for high precision SINS initial alignment

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.