A tight bound for the joint covariance of two random vectors with unknown but constrained cross-correlation
- 14 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
This paper derives a fundamental result for processing two correlated random vectors with unknown cross-correlation, where constraints on the maximum absolute correlation coefficient are given. A tight upper bound for the joint covariance matrix is derived on the basis of the individual covariances and the correlation constraint. For symmetric constraints, the bounding covariance matrix naturally possesses zero cross covariances, which further increases their usefulness in applications. Performance is demonstrated by recursively propagating a state through a linear dynamical system suffering from stochastic noise correlated with the system state.Keywords
This publication has 4 references indexed in Scilit:
- Simultaneous map building and localization for mobile robots: a multisensor fusion approachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Building a global map of the environment of a mobile robot: the importance of correlationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- New results for stochastic prediction and filtering with unknown correlationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A non-divergent estimation algorithm in the presence of unknown correlationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997