Volume 5, Issue 4 (December 2009)                   IJEEE 2009, 5(4): 215-222 | Back to browse issues page

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Mosavi M R. Infrared Counter-Countermeasure Efficient Techniques using Neural Network, Fuzzy System and Kalman Filter. IJEEE 2009; 5 (4) :215-222
URL: http://ijeee.iust.ac.ir/article-1-215-en.html
Infrared Counter-Countermeasure Efficient Techniques using Neural Network, Fuzzy System and Kalman Filter
M. R. Mosavi
Abstract:   (12963 Views)
This paper presents design and implementation of three new Infrared Counter-Countermeasure (IRCCM) efficient methods using Neural Network (NN), Fuzzy System (FS), and Kalman Filter (KF). The proposed algorithms estimate tracking error or correction signal when jamming occurs. An experimental test setup is designed and implemented for performance evaluation of the proposed methods. The methods validity is verified with experiments on IR seeker reticle based on a Digital Signal Processing (DSP) processor. The practical results emphasize that the proposed algorithms are highly effective and can reduce the jamming effects. The experimental results obtained strongly support the potential of the method using FS to eliminate the IRCM effect 83%.
Keywords: Fuzzy System , IRCCM , Jamming , Kalman Filter , Neural Network , Seeker
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Type of Study: Research Paper |
Received: 2009/12/12 | Revised: 2009/12/12 | Accepted: 2013/12/30
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.