Gaussian/non-Gaussian impulsive noises in underwater acoustic (UWA) channel seriously impact the quality of underwater acoustic communication. The common denoising algorithms are based on Gaussian noise model and are difficult to apply to the coexistence of Gaussian/non-Gaussian impulsive noises. Therefore, a new UWA noise model is described in this paper by combining the symmetric $\alpha$ -stable (S $\alpha$ S) distribution and normal distribution. Furthermore, a novel underwater acoustic signal denoising algorithm called AWMF+GDES is proposed. First, the non-Gaussian impulsive noise is adaptively suppressed by the adaptive window median filter (AWMF). Second, an enhanced wavelet threshold optimization algorithm with a new threshold function is proposed to suppress the Gaussian noise. The optimal threshold parameters are obtained based on good point set and dynamic elite group guidance combined simulated annealing selection artificial bee colony (GDES-ABC) algorithm. The numerical simulations demonstrate that the convergence speed and the convergence precision of the proposed GDES-ABC algorithm can be increased by 25% $\sim$ 66% and 21% $\sim$ 73%, respectively, compared with the existing algorithms. Finally, the experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that both the proposed wavelet threshold optimization method based on GDES-ABC and the AWMF+GDES algorithm can obtain higher output signal-to-noise ratio (SNR), noise suppression ratio (NSR), and smaller root mean square error (RMSE) compared with the other algorithms.

中文翻译：

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高斯/非高斯脉冲噪声的水下声信号降噪新算法

水下声学（UWA）通道中的高斯/非高斯脉冲噪声严重影响了水下声通信的质量。常用的降噪算法基于高斯噪声模型，难以应用于高斯/非高斯脉冲噪声的共存。因此，本文通过结合对称性描述了一种新的UWA噪声模型$ \ alpha $ -稳定（S $ \ alpha $ S）分布和正态分布。此外，提出了一种新的水下声信号去噪算法，称为AWMF + GDES。首先，通过自适应窗中值滤波器（AWMF）来自适应抑制非高斯脉冲噪声。其次，提出了一种具有新阈值函数的增强小波阈值优化算法，以抑制高斯噪声。基于最佳点集和动态精英群体指导相结合的模拟退火选择人工蜂群（GDES-ABC）算法获得最优阈值参数。数值仿真表明，所提出的GDES-ABC算法的收敛速度和收敛精度可以提高25％。 $ \ sim $ 66％和21％ $ \ sim $ 与现有算法相比，分别占73％。最后，实验结果验证了本文提出的水下声信号去噪算法的有效性，并证明了基于GDES-ABC的小波阈值优化方法和AWMF + GDES算法均可获得较高的输出信噪比（SNR）。 ，噪声抑制率（NSR）和较小的均方根误差（RMSE）。