Robust fading cubature Kalman filter and its application in initial alignment of SINS

Abstract

Strapdown inertial navigation system (SINS) is a commonly used autonomous positioning instrument. As an integral calculating system, initial alignment is an essential work of SINS. Cubature Kalman filter (CKF) is a common algorithm for nonlinear initial alignment. Under the condition of inaccurate system model and non-Gaussian observation noise, the filter will appear to unstable or even divergence. In order to solve this problem, an anti-jamming improvement suitable for initial alignment is proposed in this paper. In which the prior covariance matrix and the observation noise covariance matrix are tuned by multiple fading factors. A Chi-square test method is designed to check the filter’s state, determining the introducing method of the fading factors autonomously. Experiment results show that, the proposed algorithm performs better in terms of robustness and adaptability even under the condition of inaccurate model and non-Gaussian observation noise.