Extracting weak electromagnetic signals based on RMPSO-SPBSR in virtual anechoic chamber

Abstract

To enhance the signal extraction performance at weak noise intensity in virtual anechoic chamber, this study investigates the correlation between the generation conditions of a stochastic resonance system and its output performance. To achieve optimal resonance effects, an adaptive system called symmetric piecewise bistable stochastic resonance (SPBSR) is proposed. This system improves its structure by modifying the potential function to facilitate the occurrence of stochastic resonance. Meanwhile, it combines a regional multi-role strategy and particle swarm optimization (PSO) algorithm to determine the optimal structural parameters. The adaptive optimization process utilizes update rules that balance global and local optimal solutions, thereby mitigating the tendency to quickly converge to a local optimum. Experimental results demonstrate that the proposed system exhibits excellent performance within a noise intensity range of 0-10dB, with a correlation coefficient of over 0.75, which can effectively suppress noise interference. In practical signal processing, the system excels at accurately extracting signal characteristics, resulting in improved similarity and smoothness of the detected signal. Both simulation and experimental results validate this algorithm's strong practical relevance.