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Proceedings Paper

Fusion of data from spatially separated sensors using Riemannian manifolds
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Paper Abstract

In this paper, an approach for representing target classes in feature space using Riemannian manifolds is explored. In a formal application of the approach, it is required that several basic assumptions used in the development of differential and Riemannian geometry are satisfied. These assumptions relate to the concepts of allowable parametric representations and allowable coordinate transformations. Developing target class representations which satisfy these assumptions has a direct consequence on the selection of a suitable feature set. Having found a suitable feature set, the approach results in a natural coordinate system in which to calculate distance metrics used in classification algorithms. In this paper, the approach is applied to a situation where an active sensor and a passive sensor are spatially separated and are simultaneously collecting data on a set of targets. It is shown that the use of the natural coordinate system offered by this approach leads to a straightforward and mathematically rigorous method for fusing the sensor data at the feature level.

Paper Details

Date Published: 16 June 1997
PDF: 12 pages
Proc. SPIE 3067, Sensor Fusion: Architectures, Algorithms, and Applications, (16 June 1997); doi: 10.1117/12.276121
Show Author Affiliations
Michael Patrick Cain, Booz*Allen & Hamilton Inc. (United States)

Published in SPIE Proceedings Vol. 3067:
Sensor Fusion: Architectures, Algorithms, and Applications
Belur V. Dasarathy, Editor(s)

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