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LowComplexity Fusion Estimation Algorithms for Multisensor Dynamic Systems
Seokhyoung LEE Vladimir SHIN
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E92A
No.11
pp.29102916 Publication Date: 2009/11/01
Online ISSN: 17451337
DOI: 10.1587/transfun.E92.A.2910
Print ISSN: 09168508 Type of Manuscript: PAPER Category: Communication Theory and Signals Keyword: estimation, Kalman filtering, fusion formula, multisensor, Cholesky factorization, computational complexity,
Full Text: PDF>>
Summary:
This paper focuses on fusion estimation algorithms weighted by matrices and scalars, and relationship between them is considered. We present new algorithms that address the computation of matrix weights arising from multidimensional estimation problems. The first algorithm is based on the Cholesky factorization of a crosscovariance blockmatrix. This algorithm is equivalent to the standard composite fusion estimation algorithm however it is lowcomplexity. The second fusion algorithm is based on an approximation scheme which uses special steadystate approximation for local crosscovariances. Such approximation is useful for computing matrix weights in realtime. Subsequent analysis of the proposed fusion algorithms is presented, in which examples demonstrate the lowcomputational complexity of the new fusion estimation algorithms.

