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Proceedings Paper

Systematic error estimation in multisensor fusion systems
Author(s): Bas A. van Doorn; Henk A. P. Blom
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Paper Abstract

For multisensor fusion systems it is a prerequisite to accurately estimate and correct all systematic errors. Adequate estimation methods only exist if all systematic errors are constant random variables, while in practice they may change with time. When the object states, the systematic errors and the observations vary according to a linear Gaussian system, then one large Kalman filter forms the optimal estimator for the combined state of all object states and all systematic errors. In general the numerical complexity of this Kalman filter prohibits practical application. In order to improve this situation we decouple the large Kalman filter into a number of separate filters: for each object one track maintenance Kalman filter, and for the estimation of all sensor related systematic errors one Kalman-like filter, which we call the Macro filter. The effectiveness of this approach is illustrated through simulations for a simple example.

Paper Details

Date Published: 22 October 1993
PDF: 12 pages
Proc. SPIE 1954, Signal and Data Processing of Small Targets 1993, (22 October 1993); doi: 10.1117/12.157780
Show Author Affiliations
Bas A. van Doorn, National Aerospace Lab. NLR (Netherlands)
Henk A. P. Blom, National Aerospace Lab. NLR (Netherlands)

Published in SPIE Proceedings Vol. 1954:
Signal and Data Processing of Small Targets 1993
Oliver E. Drummond, Editor(s)

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