Anti-jamming is the core issue of wireless communication viability in complex electromagnetic environments, where jamming recognition is the precondition and foundation of cognitive anti-jamming. In the current jamming recognition methods, the existing convolutional networks are limited by the small number of layers and the extracted feature information. Simultaneously, simple stacking of layers will lead to the disappearance of gradients and the decrease in correct recognition rate. Meanwhile, most of the jamming recognition methods use single-node methods, which are easily affected by the channel and have a low recognition rate under the low jamming-to-signal ratio (JSR). To solve these problems, a multi-node cooperative jamming recognition method based on deep residual networks was proposed in this paper, and two data fusion algorithms based on hard fusion and soft fusion for jamming recognition were designed. Simulation results show that the use of deep residual networks to replace the original shallow CNN network structure can gain a 6–14% improvement in the correct recognition rate of jamming signals, and the hard and soft fusion-based methods can significantly improve the correct jamming recognition rate by about 3–7% and 5–12%, respectively, under low JSR conditions compared with the existing single-node method.

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The effective and accurate recognition of communication jamming is crucial for enhancing the anti-jamming capability of wireless communication systems. At present, a significant portion of jamming data is decentralized, stored in local nodes, and cannot be uploaded directly for network training due to its sensitive nature. To address this challenge, we introduce a novel distributed jamming recognition method. This method leverages a distributed recognition framework to achieve global optimization through federated learning. Each node independently trains its local model and contributes to the comprehensive global model. We have devised an adaptive adjustment mechanism for the mixed weight parameters of both local and global models, ensuring an automatic balance between the global model and the aggregated insights from local data across devices. Simulations indicate that our personalization strategy yields a 30% boost in accuracy, and the adaptive weight parameters further enhance the recognition accuracy by 1.1%.