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Proceedings Paper

General data fusion for estimates with unknown cross covariances
Author(s): Jeffrey K. Uhlmann
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Paper Abstract

In this paper we present a new theoretic framework for combining sensor measurements, state estimates, or any similar type of quantity given only their means and covariances. The key feature of the new framework is that it permits the optimal fusion of estimates that are correlated to an unknown degree. This framework yields a new filtering paradigm that avoids all of the restrictive independence assumptions required by the standard Kalman filter, though at the cost of reduced rates of convergence for cases in which independence can be established.

Paper Details

Date Published: 14 June 1996
PDF: 12 pages
Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243195
Show Author Affiliations
Jeffrey K. Uhlmann, Naval Research Lab. (United States)

Published in SPIE Proceedings Vol. 2755:
Signal Processing, Sensor Fusion, and Target Recognition V
Ivan Kadar; Vibeke Libby, Editor(s)

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