Multi-Domain Fusion Network for Active Jamming Recognition in Cognitive Radar

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 10; p. 1723
• Main Authors: Chen, Xiaoying, Liu, Ying, Wang, Cheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2025
• Subjects: Classification; cognitive radar; Datasets; decision fusion; Electromagnetism; Electronic warfare; Feature extraction; Fourier transforms; Image compression; Jamming; jamming recognition; Microcomputers; multi-domain feature fusion; Neural networks; Performance degradation; Pulse compression; Radar; Radar equipment; Radar systems; Recognition; Wavelet transforms; California
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17101723

Precise identification of active jamming in complex electromagnetic environments remains critically challenging for cognitive radar systems. Current methods often exhibit limitations in insufficient feature extraction and underutilization of jamming signals, leading to substantial performance degradation, particularly in low jamming-to-noise ratio (JNR) scenarios. To address these challenges, we propose a novel framework based on a multi-domain fusion network, MDFNet, to recognize 12 types of active jamming signals. MDFNet improves the recognition robustness under varying JNR conditions through a two-stage fusion of complementary features from pulse compression time–frequency (PC-TF) and range-Doppler (RD) domain images. Specifically, a novel dual-modal feature fusion (DMFF) module integrates PC-TF and RD features, while a decision fusion strategy leverages their distinctive characteristics. Experiments on typical jamming dataset demonstrate that MDFNet achieves an overall recognition accuracy of 96.05%. Notably, at a JNR of −20 dB, MDFNet outperforms the existing fusion methods by 12.86–18.19%. In summary, our proposed method significantly enhances the jamming recognition capability of cognitive radar systems in complex environments.