Inverse covariance intersection: New insights and properties
conference paper
Decentralized data fusion is a challenging task. Either it is too difficult to maintain and track the information required to perform fusion optimally, or too much information is discarded to obtain informative fusion results. A well-known solution is Covariance Intersection, which may provide too conservative fusion results. A less conservative alternative is discussed in this paper, and generalizations are proposed in order to apply it to a wide class of fusion problems. The Inverse Covariance Intersection algorithm is about finding the maximum possible common information shared by the estimates to be fused. A bound on the possibly shared common information is derived and removed from the fusion result in order to guarantee consistency. It is shown that the conditions required for consistency can be significantly relaxed, and also other causes of correlations, such as common process noise, can be treated. © 2017 International Society of Information Fusion (ISIF). China Gezhouba Group No.3 Engineering Co., Ltd (CGGC); Energy China; et al.; Hangzhou Dianzi Univeristy; LIFT; SATPRO
Topics
Covariance intersectionDecentralized data fusionEllipsoidal intersectionTrack-to-track fusionData fusionInformation fusionCommon processConservative fusionsCovariance intersectionDecentralized data fusionEllipsoidal intersectionsInformation sharedInverse covarianceTrack-to-track fusionInverse problems
TNO Identifier
781367
ISBN
9780996452700
Publisher
Institute of Electrical and Electronics Engineers Inc.
Article nr.
8009694
Source title
20th International Conference on Information Fusion, Fusion 2017. 10 July 2017 through 13 July 2017
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