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By the covariance intersection (CI) fusion method, the covariance intersection fusion steady-state Kalman filter is presented for two-sensor system with unknown cross-covariances between local filter errors. It is proved that its accuracy is higher than that of each local filtering, and is lower than that of the optimal fuser with known cross-covariances. A Monte-Carlo simulation result shows that...
It is often hard to settle the estimation problems for the signal systems with time delays. By modern time series analysis method, the systems with time delays can be transformed into those without time delays. By the measurement predictor and the white noise estimators, the local and the optimal information fusion Wiener signal estimators are presented. Applying the CI (Covariance Intersection) method,...
For the multisensor linear discrete time-invariant system, the batch fusion (BF) Kalman filtering algorithm needs the inverse operation of a high-dimensional matrix, which yields a larger computational burden and computational complexity. A sequential fusion (SF) Kalman filter is presented in this paper, which can significantly reduce the computational burden. It is equivalent to several two-sensor...
The CI (Covariance Intersection) fusion method avoids the computation of the cross covariance, and gives the consistent fused estimation. By the CI fusion method, based on the modern time series analysis method, the multichannel ARMA signal CI fusion Wiener filter is presented for the two-sensor systems with unknown cross covariance. It is proved that its estimation accuracy is higher than those of...
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