Journal Article

Compound Jamming Recognition Based on a Dual-Channel Neural Network and Feature Fusion

Zhang, Jiajia, Chen, Hui, Zhang, Liang, Li, Binbin, Wang, Yongliang, Lei, Zhenshuo, Chen, Hao

2024

Overview

Jamming recognition is a significant prior step to achieving effective jamming suppression, and the precise results of the jamming recognition will be beneficial to anti-jamming decisions. However, as the electromagnetic environment becomes more complex, the received signals may contain both suppression jamming and deception jamming, which is more challenging for existing methods focused on a single kind of jamming. In this paper, a recognition method for compound jamming based on a dual-channel neural network and feature fusion is proposed. First, feature images of compound jamming are extracted by the short-time Fourier transform and the wavelet transform. Feature images are then employed as inputs for the proposed network. During parallel processing in dual-channel, the proposed network can adaptively extract and learn task-relevant features via the attention modules. Finally, the output features in dual-channel are fused in the fusion subnetwork. Compared with existing methods, the proposed method can yield better recognition performance with less inference time. Additionally, compared with existing fusion strategies, the fusion subnetwork can further improve the recognition performance under low jamming-to-noise ratio conditions. Results with the semi-measured datasets also verify the feasibility and generalization performance of the proposed method.

• Publisher: MDPI AG
• Subject: Accuracy / Artificial intelligence / Classification / Comparative analysis / compound jamming / Control / data collection / Deception / Decision theory / extracts / feature fusion / Feature recognition / Fourier transforms / Identification and classification / image analysis / Jamming / Jamming of communications / jamming recognition / jams / learning / Machine learning / Methods / neural network application / Neural networks / Parallel processing / remote sensing / Safety and security measures / Simulation / Support vector machines / Technology application / Tracking radar / wavelet / Wavelet transforms / wills