Enhancing Subthreshold Signal Detection: A Multiscale Approach with Adaptive Noise Modeling

Music City Center 

Stochastic resonance (SR), a nonlinear phenomenon originally introduced in climate modeling, enhances signal detection by leveraging optimal noise levels within nonlinear systems. Traditional SR techniques, primarily based on single-threshold detectors, are limited to time-invariant signals and often require excessive noise for detecting weak signals, which can degrade complex signal characteristics. To address these limitations, this study explores multi-threshold systems and the application of SR in the frequency/multiscale domain using wavelet transforms. We propose a double-threshold detection system that integrates two single-threshold detectors to enhance weak signal detection. The proposed system is evaluated in both the original data and multiscale domains using simulated and real-world test signals, and its performance is benchmarked against existing detectors. Experimental results demonstrate that, in the original data domain, the proposed double-threshold detector significantly improves weak signal detection compared to conventional single-threshold approaches. Performance is further enhanced in the frequency domain, requiring lower noise levels.

Stochastic resonance

Multiscale signal processing

Fisher information

Wavelet transforms 

Section on Statistical Learning and Data Science