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Improving Navigational Accuracy for AUVs Based on an Adaptive Modified Square-Root Cubature Kalman Filter

This paper addresses an adaptive modified square-root cubature Kalman filter for the navigation of autonomous underwater vehicles (AUVs). The standard square-root cubature Kalman filter (SCKF) implements the CKF using square-root filtering to reduce computational errors. It can be modified due to the nonlinear system with a linear measurement function. The modification leads to a decrease computational complexity. Sage-Husa noise statistics estimator is combined with the Modified SCKF to estimate the unknown and changing system process noise variance. The experimental results show that compared with the MSCKF and the EKF algorithm, the adaptive MSCKF show the best accuracy for a real system with unknown process noise variance.