Weak Signal Detection Using Stochastic Resonance Principle

Employing stochastic resonance principles for weak signal detection proves to be a highly effective methodology. The main program a_b_f.m serves as the central component in this detection process. This MATLAB-based implementation encapsulates the stochastic resonance algorithm, handling signal processing through key functions that modulate noise characteristics and system parameters to enhance signal-to-noise ratio. The program executes the core stochastic resonance mechanism by appropriately tuning system potential barriers and damping coefficients, enabling weak signal amplification through nonlinear system interactions with controlled noise. Through this approach, we achieve more accurate detection of weak signals while extracting additional signal特征 information. The implementation typically involves signal preprocessing, parameter optimization routines, and resonance response analysis modules. Consequently, weak signal detection via stochastic resonance principles represents a promising and practical technique with applications across various engineering domains requiring sub-threshold signal recovery.