MGFV-SDTS: Multi-Scale Gradient and Feature-Guided Variational Framework for SAR Scene Deceptive Template Synthesis
TL;DR
Experiments on the SAR-Airport-1.0 dataset show that MGFV-SDTS preserves the intended deceptive texture better than representative baselines while maintaining visually acceptable boundary transitions, and results confirm that feature guidance retains false scene content, whereas multi-scale gradients reduce boundary incompatibility.
Abstract
Protecting target regions from reconnaissance is a critical task in synthetic aperture radar (SAR) countermeasures. For large facilities such as airports and key infrastructure, deceptive scene jamming requires more than inserting isolated false targets. It requires a credible scene template that can project the protected region as a coherent false SAR scene compatible with the surrounding background. In repeater SAR deceptive jamming, the scene template provides the amplitude modulation map for forwarded echoes. Motivated by image blending, this paper formulates deceptive jamming template construction as an SAR deceptive scene synthesis problem and proposes MGFV-SDTS, a variational optimization framework guided by multi-scale gradients and deep features. The multi-scale Laplacian guidance controls local boundary transitions between the protected region and its background. The feature-guided content and style terms preserve the selected false scene texture and its SAR statistical properties. Experiments on the SAR-Airport-1.0 dataset show that MGFV-SDTS preserves the intended deceptive texture better than representative baselines while maintaining visually acceptable boundary transitions. Ablation results confirm that feature guidance retains false scene content, whereas multi-scale gradients reduce boundary incompatibility. SAR deceptive jamming simulations on the spaceborne dataset and the measured airborne SAR case further support the applicability of the synthesized templates within the tested jamming chain.