A non-divergent estimation algorithm in the presence of unknown correlations
- 1 January 1997
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2369-2373 vol.4
- https://doi.org/10.1109/acc.1997.609105
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
This publication has 3 references indexed in Scilit:
- Semidefinite ProgrammingSIAM Review, 1996
- Suboptimal Schemes for Atmospheric Data Assimilation Based on the Kalman FilterMonthly Weather Review, 1994
- Data fusion in decentralized sensor networksControl Engineering Practice, 1994