A non-divergent estimation algorithm in the presence of unknown correlations.
Proceedings of the 1997 American Control Conference.
(pp. 2369 - 2373).
American Automatic Control Council: Evanston, US.
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.
|Title:||A non-divergent estimation algorithm in the presence of unknown correlations|
|Additional information:||Conference proceedings distributed through the IEEE, Piscataway, US|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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