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Electromagnetic Target Classification using time frequency analysis and neural networks
Date
1999-04-01
Author
Sayan, Gönül
Leblebicioğlu, Mehmet Kemal
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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This paper demonstrates the feasibility and advantages of using a self-organizing map (SOM)-type neural network classifier for electromagnetic target recognition. The classifier is supported by a novel feature extraction unit in which the Wigner distribution (WD), a time-frequency representation, is utilized for the extraction of natural-resonance-related energy feature vectors from scattered fields. The proposed target classification technique is tested for a set of canonical targets, displaying an excellent performance in terms of both real-time classification speed and accuracy, even in the presence of noisy data. (C) 1999 John Wiley & Sons, Inc.
Subject Keywords
Electrical and Electronic Engineering
,
Atomic and Molecular Physics, and Optics
,
Electronic, Optical and Magnetic Materials
,
Condensed Matter Physics
URI
https://hdl.handle.net/11511/40576
Journal
Microwave And Optical Technology Letters
DOI
https://doi.org/10.1002/(sici)1098-2760(19990405)21:1<63::aid-mop18>3.0.co;2-3
Collections
Department of Electrical and Electronics Engineering, Article
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G. Sayan and M. K. Leblebicioğlu, “Electromagnetic Target Classification using time frequency analysis and neural networks,”
Microwave And Optical Technology Letters
, pp. 63–69, 1999, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40576.