Multilevel feature-based fuzzy fusion for target recognition

Erik P. Blasch; Samuel H. Huang

doi:10.1117/12.381640

3 April 2000 Multilevel feature-based fuzzy fusion for target recognition

Erik P. Blasch, Samuel H. Huang

Proceedings Volume 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV; (2000) https://doi.org/10.1117/12.381640
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Erik P. Blasch and Samuel H. Huang "Multilevel feature-based fuzzy fusion for target recognition", Proc. SPIE 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, (3 April 2000); https://doi.org/10.1117/12.381640

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available