Robust cubature Kalman filter method for the nonlinear alignment of SINS

Abstract

Nonlinear initial alignment is a significant research topic for strapdown inertial navigation system (SINS). Cubature Kalman filter (CKF) is a popular tool for nonlinear initial alignment. Standard CKF assumes that the statics of the observation noise are pre-given before the filtering process. Therefore, any unpredicted outliers in observation noise will decrease the stability of the filter. In view of this problem, improved CKF method with robustness is proposed. Multiple fading factors are introduced to rescale the observation noise covariance. Then the update stage of the filter can be autonomously tuned, and if there are outliers exist in the observations, the update should be less weighted. Under the Gaussian assumption of KF, the Mahalanobis distance of the innovation vector is supposed to be Chi-square distributed. Therefore a judging index based on Chi-square test is designed to detect the noise outliers, determining whether the fading tune are required. The proposed method is applied in the nonlinear alignment of SINS, and vehicle experiment proves the effective of the proposed method.

Keywords

• SINS
• Nonlinear alignment
• Cubature Kalman filter
• Robust
• Multiple fading factors
• Hypothesis test