Information fusion based on fast covariance intersection filtering
W. Niehsen · 2003
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.