Mutual information-assisted feed-forward cascaded stochastic resonance for large parameter

Enhancing weak signals submerged in solid background noise is a critical research area in underwater sonar and related applications. Stochastic resonance (SR) has been proven effective in this context, and cascaded stochastic resonance (CSR) offers improved filtering performance. However, CSR faces challenges with low-frequency interference and coupling with the resonance effect of each layer. To address these issues, we propose a novel mutual information-assisted feed-forward cascaded stochastic resonance (MIFF-CSR) method for enhancing weak signals in large-parameter conditions. MIFF-CSR utilizes mutual information between the high-pass filtered SR output and the original noisy signal to mitigate low-frequency interference and exploit the beneficial properties of SR. Furthermore, MIFF-CSR decouples CSR’s dependence on each layer, progressively improving the signal-to-noise ratio by feeding forward the original signal to each cascaded SR layer. Simulations demonstrate the effectiveness of MIFF-CSR in solving low-frequency interference problems and its robust band-pass filtering characteristics for weak signal enhancement. The relationship between MIFF-CSR output and the number of cascaded layers confirms its signal enhancement capabilities. Additionally, MIFF-CSR exhibits high tolerance to the selection of cascaded layers and high-pass filter cut-off frequency. MIFF-CSR significantly enhances filtering performance, frequency response characteristics, and anti-noise capabilities compared to CSR. Application verification experiments were conducted to validate the effectiveness and practicality of the proposed method, which showed significant improvements in application performance.