An Improved Variational Adaptive Kalman Filter for Cooperative Localization

In this paper, an improved variational adaptive Kalman filter for cooperative localization with inaccurate prior information is proposed, in which the prior scale matrix of the one-step prediction error covariance matrix is adaptively estimated by using the expectation-maximization algorithm. A novel alternate iteration strategy is proposed to reduce the computational complexity of the proposed method. Convergence analysis and theoretical comparisons with the existing advanced adaptive Kalman filtering methods are also provided. Based on this, a new master-slave cooperative localization method is proposed. Lake experiment results of cooperative localization for autonomous underwater vehicles demonstrate the advantages of the proposed method over existing methods. Compared with the cutting-edge adaptive master-slave cooperative localization method, the proposed method has been improved by 22.52% in average localization error but no more than twice computational time is needed.

Automated summary

An improved variational adaptive Kalman filter is proposed for cooperative localization, with adaptively estimated prior information and a novel alternate iteration strategy to reduce computational complexity, along with theoretical comparisons. A master-slave cooperative localization method is introduced based on the proposed approach, showing advantages in experimental results. The proposed method outperforms existing methods with a 22.52% improvement in average localization error without requiring more than twice the computational time.