Self-adaption stochastic resonance weak signal detecting method based on particle swarm optimization algorithm

Abstract

The invention relates to a self-adaption stochastic resonance weak signal detecting method based on a particle swarm optimization algorithm. The method comprises the following steps of 1) particle swarm initialization; 2) step-changed stochastic resonance; 3) individual fitness evaluation; 4) particle speed and position updating; 5) termination condition judgment and 6) detection result output. The self-adaption stochastic resonance weak signal detecting method has the advantages that the simplicity is realized, the implementation is easy, the application range is wide, the convergence speed is high, high-frequency weak signals at high-noise background can be effectively detected, and a novel method is provided for stochastic resonance parameter self-adaption selection and practical application in engineering.

Description

technical field [0001] The invention relates to a weak signal detection method. In particular, it involves an adaptive stochastic resonance based on particle swarm optimization algorithm that can adaptively select the optimal variable step size stochastic resonance system structural parameters and calculation step size, and can effectively detect weak signals under large parameter conditions. Weak signal detection method. Background technique [0002] Since Benzi et al. proposed the concept of stochastic resonance (SR) in 1981 in the study of paleoclimate glaciers, the SR phenomenon has received extensive attention. The stochastic resonance phenomenon is a nonlinear phenomenon. Under certain conditions, it transfers part of the noise energy to the signal. While reducing the noise, it can resonate and strengthen the weak signal submerged in the noise, greatly improving the output signal-to-noise Ratio, so as to achieve the purpose of detecting weak signals from strong noise...

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Problems solved by technology

[0004] Aiming at weak signal detection under strong noise background and large parameter conditions, variable step size stochastic resonance can break through the limitation of adiabatic approximation theory on small parameters, so it can be applied to engineering actual signals under large parameter conditions, but how to determine the structural parameters a, The adaptive selection of b and calculation step size h is still a difficult problem, which limits the further application of stochastic resonance in engineering practice

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