Nonstationary weak signal detection based on normalization stochastic resonance with varying parameters |
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Authors: | Haibin Zhang Wei Xiong Shangbin Zhang Qingbo He Fanrang Kong |
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Affiliation: | 1.Department of Precision Machinery and Precision Instrumentation,University of Science and Technology of China,Hefei,People’s Republic of China |
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Abstract: | The nonlinear stochastic resonance system possesses the ability of taking advantage of background noise to enhance the weak signal. It provides a new approach to detect the weak signal embedded with heavy noise. This study proposes a new varying parameter stochastic resonance employing the fourth-order Runge–Kutta numerical method as well as the normalized transformation of a bistable stochastic resonance system. The model performs well in the detection of a time-varying signal with background noise for denoising and signal recovery. We take the fitness coefficient and cross-correlation coefficient as the criteria and analyze the influence of different parameters. The simulating results indicate its availability, validity and that it generates a better performance than the traditional stochastic resonance. The method develops the area of time-varying signal detection with stochastic resonance and presents new strategy for detection and denoising of a time-varying signal. It can be expected to be widely used in the areas of aperiodic signal processing, radar communication, etc. |
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