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Proceedings Paper

Maximum-likelihood approach for multisensor data fusion applications
Author(s): Yifeng Zhou; Henry Leung
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Paper Abstract

In this paper, we proposed a maximum likelihood fusion approach for multisensor fusion applications. The proposed approach was based on a parametric modeling of the noise covariance and formulated in the transformed noise subspace. It could solve the fusion problems when the sensor noises are correlated and the scaling coefficients unknown. The approach could also deal with nonstationary signals. We showed that in the optimization process, the computation of the noise parameters and the scaling coefficients were separable leading to a reduced optimization dimensionality and computational complexity. Computer simulations were used to demonstrate the effectiveness of the proposed fusion approach.

Paper Details

Date Published: 20 March 1998
PDF: 10 pages
Proc. SPIE 3376, Sensor Fusion: Architectures, Algorithms, and Applications II, (20 March 1998); doi: 10.1117/12.303678
Show Author Affiliations
Yifeng Zhou, Telexis Corp. Canada (Canada)
Henry Leung, Defence Research Establishment Ottawa (Canada)

Published in SPIE Proceedings Vol. 3376:
Sensor Fusion: Architectures, Algorithms, and Applications II
Belur V. Dasarathy, Editor(s)

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