摘要
针对传统去噪方法自适应差、对低信噪比时变信号去噪能力不足的问题,提出了一种基于循环生成式对抗网络CycleGAN的信号去噪方法。将广泛用于二维图像数据风格迁移的CycleGAN改进为适用于时序信号的一维CycleGAN,通过含噪信号和无噪信号两个数据集的循环对抗训练,得到信号从含噪空间到无噪空间的端到端最佳映射,从而获得具备自适应降噪功能的去噪模型。经过6组添加了不同信噪比的高斯白噪声的含噪信号集的测试实验,结果表明,该方法对于低信噪比的含噪时变信号具有优越的去噪能力,在信噪比和均方误差这两项指标的评价上都显著优于传统方法。
Aiming at the problems of poor adaptive denoising of traditional denoising methods and insufficient denoising ability for low signal-tonoise ratio time-varying signals,a signal denoising method based on CycleGAN was proposed.CycleGAN,which had been successfully applied to the style transfer of two-dimensional image data,was improved to a one-dimensional CycleGAN suitable for time series signals.Through the cyclical confrontation training by two data sets of noisy signal and no-noise signal,the best end-to-end mapping of the signal from the noisy space to the no-noise space was obtained,thereby obtaining a denoising model with adaptive noise reduction function.After six groups of test experiments on noisy signal sets with Gaussian white noise with different signal-to-noise ratios added,the results show that this method has superior denoising ability for high-noise time-varying signals.This method has superior denoising ability for Noisy time-varying signal with low SNR,and is significantly better than traditional methods in the evaluation of the two indicators of signal-to-noise ratio and mean square error.
作者
董骏捷
唐建
周然之
杨超越
Dong Junjie;Tang Jian;Zhou Ranzhi;Yang Chaoyue(PLA Army Engineering Universit,Nanjing 210007,China)
出处
《机电工程技术》
2021年第5期10-12,17,共4页
Mechanical & Electrical Engineering Technology
基金
国家自然科学基金资助项目(编号:51705531)
江苏省自然科学基金资助项目(编号:BK20150724)。
