System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Using covariance intersection for SLAM
Robotics and Autonomous Systems
Computational Statistics & Data Analysis
Self-tuning decoupled information fusion Wiener state component filters and their convergence
Automatica (Journal of IFAC)
Information Sciences: an International Journal
Random weighting estimation for fusion of multi-dimensional position data
Information Sciences: an International Journal
Sequential covariance intersection fusion Kalman filter
Information Sciences: an International Journal
Technical Communique: The optimality for the distributed Kalman filtering fusion with feedback
Automatica (Journal of IFAC)
New approach to information fusion steady-state Kalman filtering
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
Optimal linear estimation fusion .I. Unified fusion rules
IEEE Transactions on Information Theory
International Journal of Sensor Networks
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For multisensor systems with exactly known local filtering error variances and cross-covariances, a covariance intersection (CI) fusion steady-state Kalman filter without cross-covariances is presented. It is rigorously proved that it has consistency, and its accuracy is higher than that of each local Kalman filter and is lower than that of the optimal Kalman fuser with matrix weights. Under the unbiased linear minimum variance (ULMV) criterion, it is proved that the accuracy of the fuser with matrix weights is higher than that of the fuser with scalar weights, and the accuracy of the fuser with diagonal matrix weights is in between both of them, and the accuracies of all three weighting fusers and the CI fuser are lower than that of centralized Kalman fuser, and are higher than that of each local Kalman filter. The geometric interpretations of the above accuracy relations are given based on the covariance ellipsoids. A Monte-Carlo simulation example for tracking system verifies correctiveness of the proposed theoretical accuracy relations, and shows that the actual accuracy of the CI Kalman fuser is close to that of the optimal Kalman fuser, so that it has higher accuracy and good performance. When the actual local filtering error variances and cross-covariances are unknown, if the local filtering estimates are consistent, then the corresponding robust CI fuser is also consistent, and its robust accuracy is higher than that of each local filter.