The Application of Adaptive Square Root Cubature Particle Filtering Algorithm in Initial Alignment of Large Azimuth Misalignment in SINS

Abstract

The error model of Strap-down Inertial Navigation System (SINS) initial alignment is nonlinear under large azimuth misalignment angle condition, and it could be processed by the Particle Filter (PF) algorithm. Because the importance density function of the standard particle filter algorithm is difficult to select, a new algorithm of the Cubature Particle Filter (CPF) is proposed in this paper. In the new algorithm, the importance density function of standard particle filter is obtained by Adaptive Square root Cubature Kalman Filter (ASCKF), square root decomposition is chosen to enhance the numerical robustness and ensure that the state covariance matrix is positive definite. To meet the realtime requirement, the adaptive factor is introduced to control system model error. As the computer simulation results are shown that this algorithm could reduce the effect of inaccurate noise statistical model and it is a very effective nonlinear filtering algorithm.