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Kalman Filter is a very important tool in multi-sensor data fusion. One problem with the Kalman Filter is that it requires either that the measurements are independent or that the cross-covariance or correlation is known. However, cross-covariance among measurements from different local sensors is inevitable owning to common process noise and not easily calculated owning to insufficient information and high computational complexity. So a recent research emphasis focuses on seeking new methods of fusing state vectors and their covariance or simplifying current methods. In this paper, a new geometric fusion method called covariance coverage is proposed, which not only needn't to consider cross-covariance between measurements, but also has low computational complexity. What's more, covariance coverage method has strong extensibility and can directly support the fusion of tracking system who has more than two local sensors. Simulation experiments and results show that the accurateness of fusion state estimate by covariance coverage method is clearly higher than that of each local sensor and a little letter than that of covariance intersection algorithm proposed by J. Julier.