信号处理2025，Vol.41Issue(11)：1763-1774,12.DOI:10.12466/xhcl.2025.11.003
基于混合注意力机制和改进MobileNetV4的雷达信号去噪与识别
Radar Signal Denoising and Recognition Based on Hybrid Attention Mechanism and Enhanced MobileNetV4
摘要
Abstract
Radar signal modulation recognition plays a crucial role in acquiring information from non-cooperative sources and serves as a key basis for threat assessment in electronic reconnaissance systems.Traditional approaches for this task have typically faced two significant challenges:unsatisfactory recognition accuracy under low signal-to-noise ratio conditions and the prohibitively high computational complexity of conventional convolutional neural networks,which limits their practical deployment in resource-constrained environments.To address these limitations,this paper proposes a novel radar signal denoising and recognition method that integrates a hybrid attention mechanism with an im-proved lightweight MobileNetV4 network.The approach initially applies Choi-Williams time-frequency distribution analysis to transform 13 distinct types of radar signals into their time-frequency representations.These representations subsequently undergo grayscale conversion and normalization procedures to prepare them for network processing.Capi-talizing on the inherent sparsity of radar signals in the time-frequency domain,we developed a denoising network that in-corporates dedicated channel and spatial attention modules to extract critical features,combined with an enhanced U-Net architecture for effective noise suppression.The lightweight MobileNetV4 network is simultaneously optimized for Time-Frequency Representations(TFRs)characteristics through a custom-designed time-frequency aware frontend with multi-directional convolutional kernels and embedded attention mechanisms during feature extraction,significantly strengthening its perception of time-frequency structures.The denoising and recognition models were organically inte-grated to form an end-to-end signal processing pipeline.Simulation experiments demonstrated that the proposed denois-ing network substantially improved both peak signal-to-noise ratio and structural similarity index compared to original noisy TFRs.The improved MobileNetV4 recognition network achieved superior performance under low-SNR condi-tions,reaching 94.9%accuracy at-10 dB while maintaining only 2.57 million parameters,outperforming comparable models in both lightweight design and robustness.Experimental results confirmed that incorporating the denoising pre-processing stage effectively enhanced recognition performance in low-SNR environments.This research provides an effi-cient and practical solution for radar signal recognition in challenging electromagnetic environments.关键词
雷达信号/信号去噪/Choi-Williams分布/注意力机制/MobileNetV4Key words
radar signal/signal denoising/Choi-Williams distribution/attention mechanism/MobileNetV4分类
信息技术与安全科学引用本文复制引用
王泳欢,崔毓函,黄洁,赵闯,胡德秀..基于混合注意力机制和改进MobileNetV4的雷达信号去噪与识别[J].信号处理,2025,41(11):1763-1774,12.基金项目
国家自然科学基金(62071490)The National Natural Science Foundation of China(62071490) （62071490）
