System identification: theory for the user
System identification: theory for the user
Kalman filtering: with real-time applications (2nd ed.)
Kalman filtering: with real-time applications (2nd ed.)
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Multisensor Decision and Estimation Fusion
Multisensor Decision and Estimation Fusion
Technical Communique: The optimality for the distributed Kalman filtering fusion with feedback
Automatica (Journal of IFAC)
Track fusion with incomplete covariance information
WSEAS TRANSACTIONS on SYSTEMS
Networked data fusion with packet losses and variable delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information fusion estimation of noise statistics for multisensor systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Correlated measurement fusion Kalman filters based on orthogonal transformation
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
An optimal sequential filter for the linear system with correlated noises
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Reduced dimension weighted measurement fusion Kalman filtering algorithm
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Steady-state optimal measurement fusion white noise deconvolution estimators
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Insensitive reliable H∞ filtering against sensor failures
Information Sciences: an International Journal
Automatica (Journal of IFAC)
Optimal sequential and distributed fusion for state estimation in cross-correlated noise
Automatica (Journal of IFAC)
Hi-index | 22.15 |
When there is no feedback from the fusion center to local sensors, we present a distributed Kalman filtering fusion formula for linear dynamic systems with sensor noises cross-correlated, and prove that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements, therefore, it achieves the best performance. Then, for the same dynamic system, when there is feedback, a modified Kalman filtering fusion with feedback for distributed recursive state estimators is proposed, and prove that the fusion formula with feedback is, as the fusion without feedback, still exactly equivalent to the corresponding centralized Kalman filtering fusion formula; the various P matrices in the feedback Kalman filtering at both local filters and the fusion center are still the covariance matrices of tracking errors; the feedback does reduce the covariance of each local tracking error.