Year: 2025
Type: article
Abstract: 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.
Source: Remote Sensing
DOI: 10.3390/rs17101723
Authors: Xiaoying Chen, Ying Liu, Cheng Wang
Institution: University of Chinese Academy of Sciences
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
Award: G5228913739
OpenAlex ID: W4410360359