提出了一种全新的基于时频原子特征的雷达辐射源信号识别方法.训练阶段，在过完备时频原子库的基础上，以类区分度为度量，提取少数最能区分不同类别信号的时频原子作为一组固定的特征; 识别阶段，以原子和信号的内积的绝对值作为分类器的输入特征，采用有监督模糊自适应共振网络进行辐射源的自动识别.对5类典型雷达辐射源信号的实验结果表明，该方法大大减小了识别过程中特征提取的计算量，输入特征具有类内聚集性强、类间区分度大的特点，在信噪比大于3 dB时可以获得高的识别正确率.

A novel method for radar emitter signal recognition based on time-frequency atom feature is presented. During training, based on the over-complete time-frequency atom dictionary, a few atoms which can separate different kinds of signals best are extracted as a set of fixed feature according to the class separability. During testing, the module of inner product between atoms and signals is used as the input feature for the fuzzy ARTMAP classifier, and the radar emitter signals can be recognized automatically. Experimental results of five kinds of typical radar emitter signals show that this method reduces the computational amount of feature extraction during testing obviously, and the input features have strong concentration within classes and large separability between classes. Our method can achieve high recognition accuracy at the SNR larger than 3 dB.