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

• Published in: Signal processing Vol. 169; p. 107393
• Main Authors: Zhu, Mengtao, Li, Yunjie, Pan, Zesi, Yang, Jian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2020
• Subjects: Compound signal recognition; Multi-label convolutional neural network; Multi-label learning; Multiuser automatic modulation classification; Multi-label learning; Multiuser automatic modulation classification; Compound signal recognition; Multi-label convolutional neural network
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2019.107393

•A novel deep multi-label based framework including signal preprocessing, multi-label convolutional neural network construction and multi-decision thresholds optimization is proposed for automatic recognition of compound signals.•Multi-label learning is firstly introduced for modulation recognition filed to solve the drawbacks of enlarged model size, degraded performance, artificially defined new classes and coarse classification results in traditional multi-class methods.•A smaller model size, better total performance, good extensibility for unseen signal combinations and fine-grained analysis for recognition results is obtained compared with state of the art multi-class mAMC method. 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.