Purpose

In engineering, medicine, ocean and other signal detection, signal features are often submerged in strong background noise and difficult to extract.

Methods

A novel method based on wavelet transform and the bistable stochastic resonance (SR) with optimized parameters is proposed for weak signal detection. First, the original signal is preprocessed using wavelet decomposition and reconstruction through the bandwidth of the measured signal. Wavelet coefficients are adjusted according to the variance of each detail and approximate coefficient and the frequency band where the signal is located. Second, the parameters of the stable SR model are selected using the prior information of the signal, and then adjust the model parameters according to the calculated local signal-to-noise ratio. Finally, the bistable SR system with optimized parameters is used to process the reconstructed signal.

Conclusion

Simulation and experimental results show that the proposed method can effectively detect weak signal features buried in heavy noise.

中文翻译：

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基于小波变换和双稳态随机共振模型参数选择的多频弱信号检测

目的

在工程，医学，海洋和其他信号检测中，信号特征通常淹没在强烈的背景噪声中且难以提取。

方法

提出了一种基于小波变换和参数优化的双稳态随机共振（SR）的新型弱信号检测方法。首先，原始信号通过小波分解和重构通过被测信号的带宽进行预处理。根据每个细节的方差和近似系数以及信号所处的频带来调整小波系数。其次，使用信号的先验信息选择稳定的SR模型的参数，然后根据计算出的局部信噪比调整模型参数。最后，使用具有优化参数的双稳态SR系统来处理重构信号。

结论

仿真和实验结果表明，该方法可以有效地检测出重噪声中的弱信号特征。