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中文摘要：
有效的信号特征提取是高精度雷达辐射源识别的基础,以脉冲描述字为代表的传统特征已无法满足复杂电磁环境的需要.本文提出一种基于自适应噪声完备集合经验模态分解（CEEMDAN）的有效雷达辐射源脉内细微特征提取算法.雷达信号由对非平稳、非线性信号尤为有效的CEEMDAN分解产生的个别分量重构,抑噪效果通过1 000次蒙特卡罗实验得到验证,同时设计基于该重构的一种脉内特征空间.本文方法与主流特征提取方法的识别精度在6部雷达辐射源产生的3000个不同脉内调制的加噪信号样本上进行了实验对比,结果表明不同种类信号样本在本文特征空间中清晰可分,本文方法较之主流方法更加精确,尤其在0 dB信噪比（SNR）下仍保持90％以上的高精度.
英文摘要：
Effective signal feature extraction builds the foundation of highly accurate radar emitter identi- fication, a key function of the electronic warfare. Conventional features used in practice such as the pulse de- scription words cannot fulfill the task in complex electromagnetic environments. An effective subtle intrapulse radar feature extraction method based on the complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） was proposed. Radar signals were reconstructed by components provided by the CEEM- DAN decomposition process, which was highly effective for non-stationary and nonlinear signals; the de-noi- sing effect of the reconstruction on radar signals was validated through 1 000 Monte Carlo experiments, and an intrapulse feature space based on the reconstruction was designed. The identification accuracy of the proposed feature space was compared to that of the mainstream methods in the area, on 3 000 noise-contaminated signal samples supposed to be generated by 6 radar emitters, with different intrapulse modulation. Experimental re- sults show that the samples are totally distinguishable in the proposed feature space, and the proposed method is more accurate in the comparison, especially in highly noisy environment, with accuracy above 90% at 0 dB signal to noise ratio （SNR）.
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