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 scale matrix of one-step prediction error covariance adaptively estimated by using expectation-maximization algorithm. A novel alternate iteration strategy proposed to reduce computational complexity method. Convergence analysis and theoretical comparisons existing advanced filtering methods are also provided. Based on this, a new master-slave method proposed. Lake experiment results autonomous underwater vehicles demonstrate advantages over methods. Compared cutting-edge method, has been 22.52% average but no more than twice time needed.