An improved SVDU-IKPCA algorithm for Specific Emitter Identification
An improved SVDU-IKPCA algorithm for Specific Emitter Identification
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DOI：
10.1109/icinfa.2008.4608087
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
Dan Xu;Bo Yang;Wenli Jiang;Yiyu Zhou
中科院分区：
文献类型：
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作者：
Dan Xu;Bo Yang;Wenli Jiang;Yiyu Zhou
提出了一种核主元分析（KPCA）预测学习方法，用于辐射源识别（SEI）。通过构造核矩阵的对称分解，推导出一种新的增量核主元分析算法。在此基础上，通过建立虚拟样本，其核向量是核矩阵的外推，从而提高了预测能力。在SEI数值实验中验证了算法的先进性。
A forecast learning method of kernel principal component analysis (KPCA) is presented for specific emitter identification (SEI) application. By constructing a symmetrical decomposition of the kernel matrix, we derived a new algorithm of incremental KPCA. Based on it, the forecast capability is developed by creating dummy samples whose kernel vectors are an extrapolation of the kernel matrix. The advance of the algorithm is verified in the SEI numerical experiment.