信号处理2026，Vol.42Issue(8)：1185-1198,14.DOI:10.12466/xhcl.2026.08.001
融合UNet与扩散模型的雷达回波图像复原方法
Radar Echo Image Restoration with UNet and a Diffusion Model
摘要
Abstract
Airborne pulse-Doppler radar plays a crucial role in target detection,environmental perception,and guidance monitoring.However,existing equipment is highly susceptible to multi-source complex electromagnetic interference from wireless communications,broadcasting,and electronic countermeasures within its operating frequency band.Such interference leads to signal distortion,noise amplification,and texture degradation in radar echoes,which severely re-stricts imaging quality and the accuracy with which motion parameters can be estimated.To address this problem,we propose a two-stage interference suppression and image reconstruction framework that integrates a deterministic model based on a UNet architecture with a denoising diffusion probabilistic model(DDPM).In the first stage,the UNet model performs preliminary structural restoration and energy redistribution through multiscale feature encoding and skip con-nections to ensure accurate recovery of the global morphology.In the second stage,a conditional diffusion model intro-duces a soft prior fusion strategy to enable generative enhancement of local textures and fine details without relying on explicit masks to achieve flexible reconstruction.The proposed method was systematically evaluated under conditions with varying signal-to-noise ratio(SNR)and signal-to-jamming ratio(SJR)using simulated radar datasets containing multiple types of interference including comb-spectrum,noise-convolution interference,spectral diffusion,and slice re-construction interference.The experimental results demonstrate that the proposed framework significantly outperformed approaches based on a single model in terms of structural consistency,texture fidelity,and perceptual quality.In sce-narios with high levels of interference,the method achieved an average improvement in peak SNR(PSNR)of approxi-mately 2.1 dB and a 48%reduction in learned perceptual image patch similarity(LPIPS).Furthermore,the results of pa-rameter estimation experiments validate that the reconstructed images substantially improved the estimation accuracy of target altitude,velocity,and pitch angle,with the mean absolute percentage error(MAPE)in pitch estimation reduced by more than 45%.These results indicate that the proposed two-stage method effectively balanced deterministic regres-sion and probabilistic generation to achieve an optimal trade-off between structural accuracy and perceptual realism.Thus,the proposed approach provides a robust and efficient solution for radar image restoration and motion parameter inversion under complex interference environments.关键词
脉冲多普勒雷达/干扰抑制/扩散模型/两阶段复原/运动参数估计Key words
pulse-Doppler radar/interference suppression/diffusion model/two-stage reconstruction/motion param-eter estimation分类
信息技术与安全科学引用本文复制引用
南苏桐,刘子源,张家炜,何屹廷,谷源涛..融合UNet与扩散模型的雷达回波图像复原方法[J].信号处理,2026,42(8):1185-1198,14.基金项目
国家自然科学基金(U2230201) The National Natural Science Foundation of China(U2230201) （U2230201）
