非高斯环境下的深度学习脉冲信号去噪与重构*

侧扫声呐进行沉底小目标探测时,底混响是主要背景干扰。底混响通常是一种非平稳、非高斯的带限噪声,它使得白噪声条件下的滤波器性能受到限制。在混响背景下常利用自回归模型对接收信号进预行白化处理,但对于实际侧扫声呐应用,白化后直接匹配滤波的处理效果不甚理想。针对此问题,在自回归模型预白化的基础上,提出采用一种次最佳检测与多分辨二分奇异值分解相结合的改进方法。该方法首先对接收信号进行分段处理,利用改进Burg算法估计每段数据自回归模型的系数及阶数;然后构造白化滤波器对分段数据预白化,并对白化后的数据进行多分辨二分奇异值分解;最后应用ostu方法对原始声图和处理后的声图进行目标检测。仿真与实验结果表明,该方法明显提高了信混比,改善了侧扫声呐沉底静态小目标的成图质量,有利于后期实现基于图像的目标自动检测。

Using deep learning to de-noise and reconstruct pulse signals in non-Gaussian environment

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.