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Weak Signal Detection Based on Lifting Wavelet Threshold Denoising and Multi-Layer Autocorrelation Method

Yuzhe Hou 1, Shunming Li 1,2, Huijie Ma 1, Siqi Gong 1, and Tianyi Yu 1
1. College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2. School of Automotive Engineering, Nantong Institute of Technology, Nantong, China

Abstract—To solve the problem that the weak signal is difficult to detect under a strong background noise, a detection method based on lifting wavelet threshold denoising and multi-layer autocorrelation method is proposed. Firstly, the original signal is denoised by lifting wavelet threshold to improve the signal-to-noise ratio. Secondly, the multi-layer autocorrelation function of the noise-reconstructed signal is calculated, and its time-frequency signature are analyzed. Finally, the combined algorithm is used on weak signals with low signal-to-noise ratio to extract weak signal features. Simulation and experimental results demonstrate that the proposed method can detect weak signal features buried in the heavy noise effectively. The proposed method is compared with the traditional noise reduction method, which reflects its effectiveness and superiority.
 
Index Terms—Weak signal detection, lifting wavelet, threshold function, multi-layer autocorrelation

Cite: Yuzhe Hou, Shunming Li, Huijie Ma, Siqi Gong, and Tianyi Yu, "Weak Signal Detection Based on Lifting Wavelet Threshold Denoising and Multi-Layer Autocorrelation Method," Journal of Communications vol. 17, no. 11, pp. 890-899, November 2022. Doi: 10.12720/jcm.17.11.890-899

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.

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