Electromagnetic Target Classification using time frequency analysis and neural networks

Date

1999-04-01

Author

Sayan, Gönül
Leblebicioğlu, Mehmet Kemal

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This work is licensed under a 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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Citation Formats

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.