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Proceedings Paper

Multilevel feature-based fuzzy fusion for target recognition
Author(s): Erik P. Blasch; Samuel H. Huang
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Paper Abstract

Information fusion includes the integration of feature data, expert knowledge, and algorithms. For example, in automatic target recognition features of size, color, and motion can be fused to assess the combination of multi-modal information. A neurofuzzy fusion of features captures the multilevel language content of sensory information by fusing neural network data analysis with rule-based decision making. Additionally, the neurofuzzy architecture can effectively fuse coarse and fine abstracted feature data at the content level for decision making. In this paper, we investigate a multilevel neuro-fuzzy feature-based architecture for synthetic aperture radar target recognition.

Paper Details

Date Published: 3 April 2000
PDF: 10 pages
Proc. SPIE 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, (3 April 2000); doi: 10.1117/12.381640
Show Author Affiliations
Erik P. Blasch, Air Force Research Lab. (United States)
Samuel H. Huang, Univ. of Toledo (United States)

Published in SPIE Proceedings Vol. 4051:
Sensor Fusion: Architectures, Algorithms, and Applications IV
Belur V. Dasarathy, Editor(s)

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