Several Weighting Fusion Kalman Predictors with Colored Measurement Noises and their Accuracy Comparison

For two-sensor system with colored measurement noises, based on classical Kalman filtering, a covariance intersection (CI) fusion steady-state Kalman predictor without cross-covariance is presented. Under the linear unbiased minimum variance criterion, the three fusion Kalman predictors weighted by matrices, diagonal matrices and scalars are also presented respectively. Their accuracy relations are proved. The accuracy of CI fuser is higher than that of each local Kalman predictor, and lower than that of optimal fuser weighted by matrices. They can be considered as a new information fusion state observer or a new intelligent sensor. The geometric interpretation of the accuracy relations is given. A Monte-Carlo simulation example verifies the theoretical accuracy relations.