基于模糊函数代表性切片的运动雷达辐射源识别

针对运动雷达辐射源的信号特性，提出了一种基于模糊函数代表性切片特征优化的个体识别算法。首先抽取模糊函数的“近零”频偏切片作为辐射源的代表性特征子集，从而保留了运动雷达辐射源信号的稳定个性特征，然后利用直接判别比准则在代表性切片上进行核点排序，以选择最具有判别力的特征。对美国海军实验室仿真雷达数据和实测运动雷达辐射源数据的实验均表明，本文算法不仅有效地解决了现有的模糊函数全平面优化法的计算问题，而且显著提高了对运动雷达辐射源的个体识别性能。

Moving radar emitter recognition based on representative-cut feature of ambiguity function

According to the characteristics of moving radar signals, an ambiguity function (AF) representative-cut based feature extraction and optimization algorithm is presented for unintentional modulation recognition of moving radar emitters. It considers near-zero Doppler cuts of AF as a representative feature set of radar emitters and extracts the stable features of moving radar emitters effectively. Further, the direct discriminant ratio criterion is employed to preserve the most discriminating features by ranking the kernel points along the representative cut. Experimental results on real data of moving radar emitters, as well as synthetic-radar-data from USA Naval Research Laboratory, demonstrate that the proposed approach not only mitigates the computational problem in existing AF based methods, but also boosts recognition accuracy greatly.