Robust Initial Alignment Based on Unscented Information Kalman Filter

Abstract

The velocity of the odometer (OD) and Doppler velocity log (DVL) is easily contaminated by non-Gaussian noise, and the prior information of the state vector is usually hard to estimate accuracy enough, which will lead to the performance of alignment to decline or even divergent. Considering the error characteristic model has very significant nonlinear characteristics, a robust unscented information Kalman filter (RUIKF) algorithm for dynamic initial alignment is proposed in this article to suppress the influence of interference noise. The RUIKF algorithm first calculates the Mahalanobis distance (MD) between observation and the innovation of observation, and an amplification factor is designed to modify the estimation of the measurement noise covariance matrix and then take the modified measurement noise covariance matrix to update the measurement. The state error is estimated by the RUIKF to modify the strapdown inertial navigation system (SINS) navigation solution. Initial alignment experiments are carried out by the RUIKF and unscented information Kalman filter (UIKF) algorithm, respectively, under the conditions of outliers and mixed Gaussian distribution noise. The results show that the RUIKF can suppress the effect of outliers and non-Gaussian noise when the velocity of the OD (or DVL) is contaminated. The experiment results also verify the feasibility and effectiveness of the RUIKF algorithm on the SINS/DVL/OD integrated dynamic initial alignment.