Underwater target radiation signals are based on the CDAE-LMSAF

Home > Archive>Volume 46, Issue 19, 2023 >165-170

Underwater target radiation signals are based on the CDAE-LMSAF
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                        Guo YaqiGuo Yaqi
1.Shanxi Key Laboratory of Signal Capturing and Processing, North University of China，Taiyuan 030051,China； 2.State Key Laboratory of Dynamic Testing Technology, North University of China，Taiyuan 030051,China
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Wang JianWang Jian
1.Shanxi Key Laboratory of Signal Capturing and Processing, North University of China，Taiyuan 030051,China； 2.State Key Laboratory of Dynamic Testing Technology, North University of China，Taiyuan 030051,China
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Han XingchengHan Xingcheng
1.Shanxi Key Laboratory of Signal Capturing and Processing, North University of China，Taiyuan 030051,China； 2.State Key Laboratory of Dynamic Testing Technology, North University of China，Taiyuan 030051,China
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Han YanHan Yan
1.Shanxi Key Laboratory of Signal Capturing and Processing, North University of China，Taiyuan 030051,China； 2.State Key Laboratory of Dynamic Testing Technology, North University of China，Taiyuan 030051,China
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Wang ZhongzhengWang Zhongzheng
1.Shanxi Key Laboratory of Signal Capturing and Processing, North University of China，Taiyuan 030051,China； 2.State Key Laboratory of Dynamic Testing Technology, North University of China，Taiyuan 030051,China
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Affiliation:1.Shanxi Key Laboratory of Signal Capturing and Processing, North University of China，Taiyuan 030051,China; 2.State Key Laboratory of Dynamic Testing Technology, North University of China，Taiyuan 030051,China
Clc Number:TB566
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Abstract:

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.

Key words:underwater target radiation signal;signal enhancement;convolutional denoising autoencoder;adaptive filte

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Online: January 15,2024
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