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| Specific Radar Emitter Identification in Multiple Kernel Fusion Framework |
| Shi Ya Ji Hong-bing Zhu Ming-zhe Wang Lei |
| School of Electronic Engineering, Xidian University, Xi’an 710071, China |
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Abstract Using Multiple Kernel Learning (MKL) algorithms, which have the function of multi-source information fusion, this paper presents a method for specific radar emitter identification based on kernel-level information fusion. For various feature representations of radar emitter signals, the corresponding kernel functions or kernel matrices are constructed respectively, then their combination coefficients are calculated according to some criteria and the classification hyperplane of Support Vector Machines (SVM) is obtained simultaneously or independently, finally the identification of different emitters is realized. Especially, the proposed methods can effectively fuse the near-zero-doppler slices of Ambiguity Function (AF) of radar signals, getting better performance than the representative-doppler-slice of AF. The experimental results on three real radar data demonstrate the validity of the proposed methods.
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Received: 04 November 2013
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Corresponding Authors:
Shi Ya
E-mail: shiyaworld@163.com
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2014, Vol. 36 
