A New Variational Bayesian Adaptive Extended Kalman Filter for Cooperative Navigation

• Main Authors: Chengjiao Sun, Yonggang Zhang, Guoqing Wang, Wei Gao
• Format: Article
• Language: English
• Published: MDPI AG 2018-08-01
• Series: Sensors
• Subjects: extended Kalman filter (EKF); variational Bayesian; cooperative navigation; nonlinear filters
• Online Access: http://www.mdpi.com/1424-8220/18/8/2538

To solve the problem of unknown state noises and uncertain measurement noises inherent in underwater cooperative navigation, a new Variational Bayesian (VB)-based Adaptive Extended Kalman Filter (VBAEKF) for master–slave Autonomous Underwater Vehicles (AUV) is proposed in this paper. The Inverse Wishart (IW) distribution is used to model the predicted error covariance and measurement noise covariance matrix. The state, together with the predicted error covariance and measurement noise covariance matrix, can be adaptively estimated based on VB approximation. The performance of the proposed algorithm is demonstrated through a lake trial, which shows the advantage of the proposed algorithm.