Information fusion based on Kalman filtering often suffers from the lack of knowledge about cross correlations between the noise-corrupted signal sources. Covariance intersection filtering provides a general framework for information fusion with incomplete knowledge about the signal sources since it yields consistent estimates for any degree of cross correlation. However, covariance intersection filtering requires optimization of a nonlinear cost function which is a significant drawback with respect to computational complexity. Therefore, a fast covariance intersection algorithm is developed and investigated based on simulation results.
Information fusion based on fast covariance intersection filtering
01.01.2002
213215 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Information Fusion based on Fast Covariance Intersection Filtering
British Library Conference Proceedings | 2002
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