Algorithm

Control (management)

Computational complexity theory

Covariance matrix

Extended Kalman filter

Convergence (economics)

Economics

Computer science

Mathematical optimization

Fast Kalman filter

Adaptive filter

Mathematics

Enhanced Data Rates for GSM Evolution

Artificial intelligence

Kalman filter

Economic growth

Control theory (sociology)

Maximization

IEEE Sensors Journal

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.

An Improved Variational Adaptive Kalman Filter for Cooperative Localization