In high-intensity electromagnetic warfare, radar systems are persistently subjected to multi-jammer attacks, including potentially novel unknown jamming types that may emerge exclusively under wartime conditions. These jamming signals severely degrade radar detection performance. Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems. To address this challenge, this article proposes a novel open-set compound jamming cognition (OSCJC) method. The proposed method employs a detection-classification dual-network architecture, which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments. To achieve unknown jamming detection, we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples. This strategy enables the network to acquire highly generalizable jamming features, thereby accurately localizing candidate regions for individual jamming components within compound jamming. Subsequently, we introduce contrastive learning to optimize the classification network, significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space. This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types. Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method. Compared with the state-of-the-art open-set recognition methods, the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.
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