Information Fusion Kalman Filter for Two-Sensor System with Time-Delayed Measurements

Abstract

For the two-sensor linear discrete stochastic system with time-delayed measurements, an equivalent system without time-delayed measurements is obtained by introducing a new measurement process. Then a covariance intersection (CI) fusion steady-state Kalman filter is presented based on the modern time series analysis method. Compared with the optimal Kalman fusers weighted by matrices, diagonal matrices and scalars, this CI Kalman fuser avoids computing the cross-covariances among the local filtering errors. It is proved that its accuracy is higher than that of each local filter, and is lower than that of the Kalman fuser weighted by matrices. The geometric interpretations of these fusers' accuracy relations are given based on the covariance ellipses. A Monte-Carlo simulation example for target tracking system verifies the correctness of the proposed accuracy relations, i.e. the actual accuracy of the CI Kalman fuser is close to that of the fuser weighted by matrices, and is higher than that of each local filter, so it has higher accuracy and good performances.