Suboptimal fading square root cubature Kalman filter based navigation algorithm of UUV

Abstract

In order to solve the large computing cost and numerical instabilities of autonomous navigation of Unmanned underwater vehicle (UUV). A suboptimal fading square-root cubature Kalman filter (SFSCKF) is designed based on the square-root cubature Kalman filter (SCKF). The algorithm carries out prediction and observation by adopting the motion model and observation model of UUV. The fading factor is joined into the computation of the covariance matrix, and up date with square root of the covariance, which ensures the symmetry and positive definite of the covariance. Test results based on UUV lake trial data indicates that the proposed SFSCKF algorithm is valid and feasible, and provides better accuracy than the conventional navigation algorithms.