Adaptive parameter-tuning stochastic resonance based on SVD and its application in weak IF digital signal enhancement

Abstract

Parameter-tuning stochastic resonance can effectively use noise to enhance signal energy, whereas its system parameters are hard to select, and how to combine it with more practical signals needs to be researched. In this study, the IF (intermediate frequency) digital signal with low SNR (signal-noise ratio) is selected as the research object, and the measuring function based on SVD (singular value decomposition) that is not dependent on prior knowledge is proposed as the evaluation function to optimize the parameters of stochastic resonance system. The nature of the stochastic resonance is first described from the eigenspace of the signal. After the analysis of the effects of different system parameters, amplitude normalization is employed to optimize only one parameter, simplifying the algorithm. Finally, an adaptive parameter-tuning stochastic resonance method based on AFSA (artificial fish swarm algorithm) is developed for three types of modulated signals, achieving an optimum matching of noisy signals and non-linear systems at fast convergence speed. According to the simulation, the proposed algorithm is proven effective, efficient, and robust, laying a solid foundation for the subsequent signal processing work.