Prior-Guided Lightweight Dual-Task Network for Composite Active Jamming Recognition and Time-Frequency Parameter Estimation in Radar Remote Sensing

Le résumé fourni par la source

Active jamming in complex electromagnetic environments can severely degrade radar remote sensing imaging and target detection, especially when deceptive and suppressive jamming components coexist. Existing deep learning methods usually formulate jamming recognition as a closed set classification task, which provides limited information about component superposition, time-frequency localization, and physical jamming parameters. To address these limitations, this paper proposes a prior-guided lightweight dual-task network for structured composite active jamming cognition. The proposed framework extracts multi-domain handcrafted features and decision tree based coarse priors from the received signal, and fuses them with short-time Fourier transform (STFT) time-frequency images through a confidence-gated MobileViT_CA-based network. The recognition branch predicts the jamming family, fine-grained class, and composite attributes, while the segmentation branch estimates component masks for copy, convolution, and noise components. A FiLM-conditioned mask refinement module further improves mask continuity and boundary quality, enabling the extraction of physical parameters such as bandwidth, center frequency, coverage duration, delay, slice width, and repetition interval. Experiments on a 22-class active jamming dataset show that the proposed method, built on a 1.92 M-parameter MobileViT_CA backbone, achieves 93.78% overall classification accuracy, 96.49% jamming family accuracy, and 89.17% accuracy on the 12 composite classes. The FiLM-conditioned mask refinement module improves the test-set mIoU from 67.55% to 82.93%, and most representative physical parameters are estimated with relative errors below 15%.

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