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Proceedings Paper

Multisensor statistical interval estimation fusion
Author(s): Yunmin Zhu; Gan Yu; X. Rong Li
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Paper Abstract

This paper deals with multisensor statistical interval interval estimation fusion, that is, data fusion from multiple statistical interval estimators for the purpose of estimation of a parameter (theta) . A multisensor convex linear statistic fusion model for optimal interval estimation fusion is established. A Gaussian-Seidel iteration algorithm for searching for the fusion weights is proposed. In particular, we suggest convex combination minimum variance fusion that reduces huge computation of fusion weights and yields near optimal estimate performance generally, and moreover, may achieve exactly optimal performance for some specific distributions of observation data. Numerical examples are provided and give additional support to the above results.

Paper Details

Date Published: 6 March 2002
PDF: 11 pages
Proc. SPIE 4731, Sensor Fusion: Architectures, Algorithms, and Applications VI, (6 March 2002); doi: 10.1117/12.458392
Show Author Affiliations
Yunmin Zhu, Sichuan Univ. (China)
Gan Yu, Sichuan Univ. (China)
X. Rong Li, Univ. of New Orleans (United States)


Published in SPIE Proceedings Vol. 4731:
Sensor Fusion: Architectures, Algorithms, and Applications VI
Belur V. Dasarathy, Editor(s)

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