Histogram-Informed Radar PRI Modulation Recognition With Dual-Branch Network

Abstract

Analyzing and recognizing the modulation types of radar pulse repeated intervals (PRIs) is a critical aspect of electronic support measurement as it is closely associated with the operational modes of radar emitters. This study proposes the histogram-informed dual branches network (HIDB-Net) to recognize modulation types of PRI by learning the histogram patterns of PRI and DPRI simultaneously. To this end, vectorized histogram features of PRI and DPRI sequences are extracted and utilized as the input for the network. Subsequently, these features are separately processed by dual branches' 1-D convolution neural network layers and fused as a whole for modulation recognition through the multihead self-attention module. Finally, HIDB-Net infers the recognition result via a fully connected layer followed by softmax operation. In contrast to previous methods, our approach is capable of handling PRI sequences of arbitrary length with fewer feature dimensions, at the same time achieving superior recognition performance. Experimental results validate the robustness and effectiveness of our method.