Sequential CI Fusion Steady-State Suboptimal Kalman Filter for Multiple Time-Delay Systems with Correlated Noises

Abstract

It is troublesome to obtain the fused state estimation for multi-sensor multi-delay systems with correlated noises. The previous conventional fused estimation method uses the information, sent by several different filter, smoothers and predictors, to get one-step estimation, which increases the complexity of the method, so that it is not suitable for real application. In order to get more convenient estimators, the augmented state equation is introduced, and then an augmented steady-state Kalman estimator can be got, which conceals the time delays. Extracting the partial component of that augmented steady-state estimator yields the suboptimal estimator, which ignores the correlation between the components of the augmented estimator, but possesses more excellent rapidity and convenience compared with the previous fused estimator. Then by Sequential Covariance Intersection (CI) fusion method, a fast fusion steady-state suboptimal Kalman filter is obtained. Simulation examples show that although the proposed fusion steady-state estimator is suboptimal, its accuracy is higher than each of the local estimators and approximate to the optimal information fusion estimator.