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
2002-01-01
213215 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Information Fusion based on Fast Covariance Intersection Filtering
British Library Conference Proceedings | 2002
|Optimal and Self-correcting Covariance Intersection Fusion Kalman Filters
Springer Verlag | 2021
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