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Hybrid radar emitter recognition based on rough k-means classifier and SVM

Zhilu Wu1, Zhutian Yang1*, Hongjian Sun2, Zhendong Yin1* and Arumugam Nallanathan2

Author Affiliations

1 School of Electronics and Information Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China

2 Department of Electronic Engineering, King’s College London, London, WC2R 2LS, UK

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EURASIP Journal on Advances in Signal Processing 2012, 2012:198  doi:10.1186/1687-6180-2012-198

Published: 18 September 2012


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.

Emitter recognition; Rough boundary; Uncertain boundary; Training sample; Time complexity