Stochastic resonance impact signal detection method based on a novel single potential well model

Our research introduces a novel stochastic resonance (SR) model featuring a single potential well and develops a dedicated detection system designed to address the challenging problem of detecting impact signals within a highly noisy background. We begin by examining the limitations of conventional metrics, such as the cross-correlation coefficient and kurtosis index, in identifying nonperiodic impact signals, and subsequently introduce an improved metric. By harnessing parameter-adjusted SR, this innovative potential well model and metric is integrated to formulate an adaptive detection method for nonperiodic impact signals. This method automatically adjusts system parameters in response to the input signal. Subsequently, numerical simulations of the system is conducted so as to perform a comparative analysis with experimental results obtained from both asymmetric single potential well and periodic potential systems. Our findings conclusively demonstrate the enhanced effectiveness of our proposed method in detecting impact signals within a high-noise environment. Furthermore, the method provides more accurate estimates of both the intensity and precise location of the input impact signal from the output results.