- Title
- Covariance intersection for partially correlated random vectors
- Creator
- Wu, Zongze; Cai, Qianqian; Fu, Minyue
- Relation
- IEEE Transactions on Automatic Control Vol. 63, Issue 3, p. 619-629
- Publisher Link
- http://dx.doi.org/10.1109/TAC.2017.2718243
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2018
- Description
- This paper generalizes the well-known covariance intersection algorithm for distributed estimation and information fusion of random vectors. Our focus will be on partially correlated random vectors. This is motivated by the restriction of the standard covariance intersection algorithm which treats all random vectors with arbitrary cross-correlations and restriction of the classical Kalman filter which requires complete knowledge of the cross-correlations. We first give a result to characterize the conservatism of the standard covariance intersection algorithm. We then generalize the covariance intersection algorithm to two random vectors with a given correlation coefficient bound and show in what sense the resulting covariance bound is tight. Finally, we generalize the notion of correlation coefficient bound to multiple random vectors and provide a covariance intersection algorithm for this general case. Our results will make the already popular covariance intersection more applicable and more accurate for distributed estimation and information fusion problems.
- Subject
- covariance intersection; distributed estimation; multisensor data fusion; multisensor information fusion
- Identifier
- http://hdl.handle.net/1959.13/1390360
- Identifier
- uon:33034
- Identifier
- ISSN:0018-9286
- Language
- eng
- Reviewed
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