Automatic modulation recognition of compound signals using a deep multi-label classifier: A case study with radar jamming signals

The modern battlefield is getting more complicated due to the increasing number of different radiation sources as well as their fierce contention (interference) and confrontations (jamming) in the frequency spectrum. A radar, or a communication system usually has to struggle with multiple overlapped signals injected into its receiver to ensure desired system performance. Thus, the requirement for recognition of the modulation type of each constituent signal in a compound signal has emerged as a multiuser automatic modulation classification (mAMC) task in a signal processing field. This paper proposes a deep multi-label based mAMC framework (MLAMC) for compound signals which includes three serial steps, the time-frequency representation image (TFRI) extraction for signal preprocessing, multi-label convolutional neural network (MLCNN) construction for multi-label classification, and multi-decision thresholds optimization for output label decision. By applying the proposed MLAMC method on the compound radar jamming signals as a case study, the effectiveness and superiority of our proposed method are validated in four aspects of a smaller model size, better total performance, good extensibility for unseen signal combinations, and fine-grained analysis for recognition results.