Improved Signal Detection in Bistable Systems Through Stochastic Resonance Across Diverse Noise Environments

This paper presents a simulation-based analysis of stochastic resonance (SR) in bistable systems under three distinct noise conditions: Gaussian white noise, pink noise, and impulse noise. A novel contribution of this work is the introduction of a sharpness metric, defined as the second derivative of the output SNR–noise intensity curve, which quantitatively characterizes the selectivity of resonance tuning. Using numerical solutions of the overdamped Langevin equation, we show that each noise type exhibits a unique optimal intensity for maximizing signal-to-noise ratio (SNR). Our results reveal a consistent trade-off between SNR gain and resonance sharpness: Gaussian noise yields the highest signal amplification, while pink noise induces the sharpest tuning response. The analysis includes time-domain trajectories, power spectral densities, and comparisons with monostable systems, confirming that bistability is essential for SR to occur. These findings provide a unified dual-metric framework for evaluating SR quality across noise types, with implications for weak signal detection, neuromorphic systems, and adaptive noise-enhanced devices.