基于CDAE-LMSAF的水下目标辐射信号增强

针对远距离目标(如潜艇、鱼雷等)被动定位时存在海洋环境噪声、舰艇自身噪声等影响，从而导致定位精度降低的问题，本文提出了一种基于卷积去噪自编码器和自适应最小均方误差滤波(CDAE-LMSAF)的增强方法，通过提取水下目标辐射信号和含噪信号的时频谱图特征，作为卷积去噪自编码的输入进行训练和建模，再利用自适应滤波器对神经网络增强后的音频进行优化，实现对水下目标辐射信号的增强。仿真实验结果表明，在信噪比为-5 dB时，本文方法的信噪比为17.51 dB,相比于多窗谱谱减法的1.23 dB,卷积去噪自编码器的7.21 dB,自适应最小均方误差滤波的4.12 dB,本文方法具有更高的信噪比增益。

Underwater target radiation signals are based on the CDAE-LMSAF

Aiming at the problem that the passive positioning of long-distance targets (such as submarines, torpedoes, etc.) is affected by Marine environment noise and ship''s own noise, which leads to the reduction of positioning accuracy, this paper proposes an enhancement method based on convolutional denoising autoencoder and adaptive least mean square error filter (CDAE-LMSAF). By extracting the time-spectral features of the underwater target radiation signal and the noisy signal, it is trained and modeled as the input of convolutional de-noising self-coding. Then, adaptive filters are used to optimize the audio after neural network enhancement to realize the enhancement of underwater target radiation signal. The simulation results show that when the SNR is -5 dB, the SNR of the proposed method is 17.51 dB. Compared with 1.23 dB of multiwindow spectral subtraction, 7.21 dB of convolutional denoising autoencoder and 4.12 dB of adaptive least mean square error filtering, the proposed method has a higher SNR gain.