A multi-radar emitter sorting and recognition method based on hierarchical clustering and TFCN

Radar emitter recognition is an important part of the electronic attack and defense system, and its key task is to sort and identify various information of emitters from mixed pulse streams. Pulse descriptor word, as an easily obtainable feature, is often used for sorting and recognition. Aiming at the problems of high complexity and difficulty in handling intra-class clustering and inter-class aliasing of existing sorting methods, a hierarchical clustering method based on Kernel Density Estimation-Kullback-Leibler Divergence-Template Matching (KDEKLD-TM) is proposed. Firstly, the down-sampled data is used to construct central clusters, greatly improving the processing speed. Then, with probability theory as the theoretical support, inter-class clustering on all samples is performed based on maximum posterior probability. Finally, based on cluster distribution similarity and periodic template matching, intra-class merging and inter-class deinterleaving are completed. After sorting, considering the periodic differences in pulse repetition intervals among different types of emitter and the insufficient attention paid by existing recognition methods to this feature, a time-frequency convolution network (TFCN) based emitter recognition method is proposed for the first time in terms of pulse description word. Using time-frequency analysis (TFA) to extract periodic features and using convolutional neural networks (CNN) for classification, the onedimensional sequence classification problem is treated as a two-dimensional image classification problem. The proposed method is simulated in a typical scenario with intra-class clustering and inter-class aliasing. The results show that the proposed method can sort a total of 2.06 million aliased samples composed of 10 classes within 58.61 s, and the recognition accuracy reaches 96.33%. The comparison with the baseline method proves the effectiveness and progressiveness of the proposed method. Finally, the sorting and recognition performance of the proposed algorithm in complex scenarios in the presence of pulse loss is discussed.