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
On the fusion of imprecise uncertainty measures using belief structures
Information Sciences: an International Journal
Tracking a moving object via a sensor network with a partial information broadcasting scheme
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)
Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
A variational Bayesian approach to robust sensor fusion based on Student-t distribution
Information Sciences: an International Journal
Information Sciences: an International Journal
Insensitive reliable H∞ filtering against sensor failures
Information Sciences: an International Journal
International Journal of Sensor Networks
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For multisensor system with unknown cross-covariances among local estimation errors, the batch covariance intersection (BCI) fusion estimation algorithm requires the optimization of a multi-dimensional nonlinear cost function, which yields a larger computational burden and computational complexity. A fast sequential covariance intersection (SCI) Kalman filtering algorithm is presented in this paper, which only requires to solve the optimization problem of several one-dimensional nonlinear cost functions. It is equivalent to several two-sensor covariance intersection (CI) Kalman fusers, and is a recursive two-sensor CI Kalman fuser. Its accuracy depends on the orders of sensors. It is proved that the SCI fuser is consistent, and its accuracy is higher than that of each local estimator and is lower than that of the optimal Kalman fuser with known cross-covariances. The geometric interpretation of accuracy relations based on the covariance ellipses is given, and the properties of the covariance ellipses are rigorously proved. Two Monte-Carlo simulation examples show the effectiveness of the proposed results, show that the accuracies of the SCI fusers are not very sensitive with respect to the orders of sensors, and show that its actual accuracy is close to that of the optimal Kalman fuser in general cases, and its robust accuracy is close to that of the BCI fuser, so it has good performances.