A novel partitioned matrix‐based parameter update method embedded in variational Bayesian for underwater positioning

Abstract

In order to meet the requirements of the high-precision positioning for the autonomous underwater vehicles (AUVs) in the complex and time-varying marine environments, a novel partitioned matrix-based parameter update method embedded in variational Bayesian (PMPU-VB) is proposed to deal with the positioning accuracy problem caused by the inaccurate predicted error covariance and measurement noise matrices. By employing the variational Bayesian (VB) method, the accurate predicted error covariance matrix and the measurement noise matrix can be obtained. Subsequently, the PMPU-VB, which employs the accurate predicted error covariance matrix and measurement noise matrix, is used as a substitute of the traditional Gaussian filtering (GF) algorithm, to update the probability density function (PDF) of the state vector. The state vector is defined to follow the Gaussian distribution, and the parameters of the Gaussian distribution are deduced by using the proposed partitioned matrix-based parameter update method. Finally, the accurate position information of the AUV can be obtained. Therefore, the more precise state vector and the error covariance matrix are acquired. The experiments results illustrate that the PMPU-VB has higher estimate accuracy, better stability and robustness than other comparison algorithms.