Sequential Fast Covariance Intersection Fusion Kalman Filter for Multi-Sensor Systems with Random One-step Measurement Delays and Missing Measurements

In order to handle the fusion estimation problem for the multi-sensor systems with random one-step measurement delays and missing measurements, a Sequential Fast Covariance Intersection (SFCI) fusion structure is presented by the augmented state technology with the fictitious noises, which can avoid large computational burden about the unknown cross-covariance matrices and is not sensitive to the sequential fusion orders. The accuracy of the presented SFCI fusion Kalman filter is higher than each of local estimators, less but close to that of the information fusion Kalman estimator weighted by matrices. The simulation example shows the effectiveness and the estimation accuracy of the proposed algorithm.