Anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base

The performance of a strapdown inertial navigation system (SINS) largely depends on the accuracy and rapidness of the initial alignment. A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper. The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions, the SINS alignment is heuristically established as an optimization problem of finding the minimum eigenvector. In order to further improve the alignment precision, an adaptive recursive weighted least squares (ARWLS) curve fitting algorithm is used to fit the translational motion interference-contaminated reference vectors according to their time domain characteristics. Simulation studies and experimental results favorably demonstrate its rapidness, accuracy and robustness.