Ambiguity function (AF) modeling of radar signals is a powerful approach to feature extraction and recognition of radar emitters. An AF subspace based optimization framework is proposed to identify radar emitters by exploring unintentional modulation on pulse (UMOP) features. First, near-zero Doppler cuts of AF were extracted as a preliminary feature subset. Then, two kinds of cut-concatenation schemes were designed to construct two different pairs of feature vectors with complementary information respectively, which will facilitate the subsequent feature fusion via canonical correlation analysis (CCA) or discriminative canonical correlation analysis (DCCA). Theoretical analysis and experimental results show that the proposed algorithms not only alleviate the calculation problem in the existing AF based method, but also improve the recognition performance considerably, due to the successful information fusion and redundancy reduction conducted in the AF subset.%对雷达辐射源信号进行模糊函数建模是一种有效的特征提取途径,通过对无意调制的雷达辐射源信号的模糊函数分析,提出了基于模糊函数子空间特征优化的个体识别方法,首先抽取模糊函数的"近零"频偏切片作为辐射源信号的主要特征,继而设计了切片串联策略构建了互补的特征子集对,从而分别利用典型相关分析和鉴别典型相关分析实现了切片特征的融合,理论分析和对实测数据的实验结果表明,所提算法不仅克服了现有的全平面核点排序法的计算问题,而且有效地融合了模糊函数各近零切片上的互补信息,在显著提高辐射源个体识别性能的同时,进一步消除了模糊函数特征的冗余性.
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