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Covariance Intersection in Nonlinear Estimation Based on Pseudo Gaussian Densities

Noack, Benjamin; Baum, Marcus; Hanebeck, Uwe D.

Many modern fusion architectures are designed to process and fuse data in networked systems. Alongside the advantages, such as scalability and robustness, distributed fusion techniques particularly have to tackle the problem of dependencies between locally processed data. In linear estimation problems, uncertain quantities with unknown cross-correlations can be fused by means of the covariance intersection algorithm, which avoids overconfident fusion results. However, for nonlinear system dynamics and sensor models perturbed by arbitrary noise, it is not only a problem to characterize and parameterize dependencies between estimates, but also to find a proper notion of consistency. This paper addresses these issues by transforming the state estimates to a different state space, where the corresponding densities are Gaussian and only linear dependencies between estimates, i.e., correlations, can arise. These pseudo Gaussian densities then allow the notion of covariance consistency to be used in distributed nonlinear state estimation.

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Volltext §
DOI: 10.5445/IR/1000035130
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2011
Sprache Englisch
Identifikator ISBN: 978-1-4577-0267-9
KITopen-ID: 1000035130
Erschienen in Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, 5-8 July 2011
Seiten 8 S.
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