Sequential Covariance Intersection Fusion Kalman Filter for Multi-Sensor Systems with Multiple Time Delayed Measurements

In order to handle the fusion estimation problem for the multi-sensor systems with multiple time delayed measurements, a steady-state suboptimal Kalman filter is derived, and then a sequential covariance intersection (SCI) fusion Kalman filtering algorithm is presented, which can avoid large computational burden and handle the fusion problem for the systems with unknown cross-covariances. The simulation example shows the effectiveness, consistency and the geometric interpretation of the accuracy relation. The accuracy of the presented SCI fusion Kalman filter is higher than each of local estimators and is close to that of the Kalman fuser weighted by matrices, so it has good performance.