针对传统干扰抑制算法在复杂电磁环境中依赖干扰参数精确估计、对不同干扰类型需分别设计处理策略、泛化能力弱等不足,本文提出一种基于时频图像输入的U-Net干扰抑制算法。该算法利用U-Net网络在图像特征提取与端到端学习中的优势,将接收信号的短时傅里叶变换时频图作为网络输入,通过编码-解码结构与跳跃连接实现对干扰分量的精准定位与有效抑制,同时最大程度保留目标信号的原始特征。在单音干扰、多音干扰、窄带干扰及线性调频干扰这四种典型弱干扰场景下,将本文算法与传统频域抑制方法及残差网络抑制方法进行对比。结果表明,所提U-Net算法在不同干扰类型下均表现出最优的误码率性能,相较于传统算法可获得约1dB的性能增益,相较于残差网络亦有约0.5dB的提升,且在信干比为0dB与5dB条件下均保持稳定,验证了算法在弱干扰环境下的强鲁棒性与良好的通用性。

The traditional interference suppression algorithms suffer from the shortcomings of relying on the accurate estimation of interference parameters in complex electromagnetic environments,needing the separate processing strategies for different types of interference,and weak generalization ability,This paper proposes a U-Net-based interference suppression algorithm using time-frequency image input.The proposed algorithm leverages the advantages of U-Net in image feature extraction and end-to-end learning by taking the short-time Fourier transform time-frequency image of the received signal as network input.The precise localization and effective suppression of interference components are achieved by using an encoding-decoding structure with skip connections,while preserving the original features of the target signal to the greatest extent.The proposed algorithm is compared with the conventional frequency-domain suppression methdod and the residual network-based suppression method in terms of four typical weak interferences,namely single-tone,multi-tone,narrowban and linear frequency modulation (LFM) interferences.The results show that the proposed U-Net-based algorithm exhibits an optimal bit error rate performance under different types of interference.It achieves approximately 1dB performance gain compared to traditional algorithms and about 0.5dB improvement over the residual network,and maintains stability under signal-to-interference ratios of 0dB and 5dB,thus verifying the strong robustness and good generalization of the proposed algorithm in weak interference environments.