| 李悦,马晓川,王磊,刘宇.非高斯环境下的深度学习脉冲信号去噪与重构*[J].,2021,40(1):142-146 |
| 非高斯环境下的深度学习脉冲信号去噪与重构* |
| Using deep learning to de-noise and reconstruct pulse signals in non-Gaussian environment |
| 投稿时间：2020-04-28 修订日期：2021-01-06 |
| 中文摘要: |
| 由于实际海洋环境中存在大量的非高斯噪声，一些基于高斯假设的传统去噪方法在实际海洋环境中性能下降甚至失效。针对非高斯噪声，如 稳定分布噪声、非平稳行船噪声下的脉冲信号的去噪与重构，本文提出一种基于深度学习的方法。去噪模型首先通过学习带噪信号短时傅里叶变换谱与残差谱之间的映射关系以去除环境噪声，之后对去噪信号的时频谱进行逆变换重构脉冲信号。仿真实验结果表明，深度学习模型在非高斯噪声环境下脉冲信号的去噪与重构任务中有着良好的表现，在实测样本上也表现出良好的泛化性，体现了一定的工程应用价值。 |
| 英文摘要: |
| Due to the ubiquitous non-Gaussian noise in marine environments, traditional denoising methods based on Gaussian assumption may degrade or even fail in these circumstances. A deep learning based method is proposed in this paper, aiming at the denoising and reconstruction problem of pulse signals in non-Gaussian noise, including -stable distributed noise and non-stationary ship noise. First, the mapping relationship between the short-time Fourier transform characteristics of noisy signal and the residual signal is learned for environmental noise removal. Then, the reconstructed signal is obtained through the inverse short-time Fourier transform on the denoised time-frequency spectrum. Simulation results show that the proposed method has a good performance in denoising and reconstruction tasks of pulse signals in non-Gaussian noise circumstances, as well as good generalization on the test sample in actual situation. Therefore, it shows promising prospect in engineering application. |
| DOI：10.11684/j.issn.1000-310X.2021.01.016 |
| 中文关键词: 深度学习，非高斯噪声，信号去噪 |
| 英文关键词: Deep learning, Non-Gaussian noise, Signal denoising |
| 基金项目: |
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