Abstract
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.
Highlights
With the continuous expansion of marine exploitations and the increasing complexity of military requirements, it will be difficult for a single Autonomous Underwater Vehicle (AUV) to achieve the desired goals
In the master–slave cooperative navigation, it is generally considered that the AUVs with strong positioning ability are the master AUVs, and the master AUVs are the core of the AUV group that each slave AUV needs to communicate with
In order to solve the inaccurate measurement noise generated by underwater acoustic communication, and uncertain process noise during dead reckoning, this paper models the Inverse Wishart (IW) prior to the prediction error covariance matrix and measurement noise covariance matrix
Summary
With the continuous expansion of marine exploitations and the increasing complexity of military requirements, it will be difficult for a single Autonomous Underwater Vehicle (AUV) to achieve the desired goals. Similar to a single AUV, the multi-AUV cooperative operation requires accurate navigation and localization abilities [4,5]. In the master–slave cooperative navigation, it is generally considered that the AUVs with strong positioning ability are the master AUVs, and the master AUVs are the core of the AUV group that each slave AUV needs to communicate with. This cooperative navigation method has many advantages, such as low cost, easy realization, group resolution and flexible combination [6,7].
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