FOA-based QR-factorized CKF Algorithm for AUV Navigation

Navigation is one of the key technologies for autonomous underwater vehicles (AUVs). To improve the accuracy of the navigation system, it is necessary to select an appropriate algorithm to fuse the navigation parameters. This paper selects the Cubature Kalman Filter (CKF) as the fusion algorithm. Different from the basic CKF, this paper uses the QR decomposition method to update the error covariance matrix of the CKF, which improves the numerical stability and accuracy of the algorithm. Since the process noise covariance matrix and the measurement noise covariance matrix are very important to the accuracy and stability of the navigation system, this paper uses FOA to adjust its parameters. Based on the data collected by AUV, the navigation algorithm proposed in this paper is verified, and the accuracy and reliability of the navigation system are proved.