Hybrid Radar Emitter Recognition Based on Rough K-Means Classifier and SVM
Wu, Z.; Yang, Z.; Sun, Hongjian; Yin, Z.; Nallanathan, A.
Professor Hongjian Sun email@example.com
Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this article, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, i.e., the primary signal recognition and the advanced signal recognition. In the former step, the rough k-means classifier is proposed to cluster the samples of radar emitter signals by using the rough set theory. In the latter step, the samples within the rough boundary are used to train the support vector machine (SVM). Then SVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and has a lower time complexity than the traditional approaches.
Wu, Z., Yang, Z., Sun, H., Yin, Z., & Nallanathan, A. (2012). Hybrid Radar Emitter Recognition Based on Rough K-Means Classifier and SVM. EURASIP Journal on Advances in Signal Processing, 2012, Article 198. https://doi.org/10.1186/1687-6180-2012-198
|Journal Article Type||Article|
|Acceptance Date||Sep 2, 2012|
|Publication Date||Sep 18, 2012|
|Deposit Date||Apr 16, 2013|
|Publicly Available Date||Oct 29, 2015|
|Journal||EURASIP Journal on Advances in Signal Processing|
|Peer Reviewed||Peer Reviewed|
|Keywords||Emitter recognition, Rough boundary, Uncertain boundary, Training sample, Time complexity.|
Published Journal Article
Publisher Licence URL
© 2012 Wu et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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