Abstract
By the CI (Covariance Intersection) fusion algorithm, based on the ARMA innovation model, the two-sensor CI fusion Kalman estimators are presented for the systems with unknown cross-covariance. It is proved that their estimation accuracies are higher than those of the local Kalman estimators, and are lower than those of the optimal fused Kalman estimators. A Monte-Carlo simulation result shows that the actual accuracy of the presented CI fusion Kalman estimator are close to those of the optimal fused Kalman estimators with known cross-covariance.
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