Title :
A non-divergent estimation algorithm in the presence of unknown correlations
Author :
Julier, Simon J. ; Uhlmann, Jeffrey K.
Author_Institution :
Robotics Res. Group, Oxford Univ., UK
Abstract :
This paper addresses the problem of estimation when the cross-correlation in the errors between different random variables are unknown. A new data fusion algorithm, the covariance intersection algorithm (CI), is presented. It is proved that this algorithm yields consistent estimates irrespective of the actual correlations. This property is illustrated in an application of decentralised estimation where it is impossible to consistently use a Kalman filter
Keywords :
filtering theory; sensor fusion; state estimation; covariance intersection algorithm; cross-correlation; data fusion algorithm; nondivergent estimation algorithm; random variables; unknown correlations; Covariance matrix; Information filtering; Information filters; Predictive models; Random variables; Sensor fusion; Sensor systems; State estimation; Vehicles; Yield estimation;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.609105
