基于改进GOOSE算法的VMD体征信息研究
Research on VMD of physical signs information based on improved GOOSE algorithm
刘贵 1徐曦 1许中华 1谭奥成1
作者信息
- 1. 湖南工业大学计算机学院,湖南株洲412000
- 折叠
摘要
本文中依靠60GHz调频连续波(FMCW)毫米波雷达,通过改进的GOOSE算法,在种群初始化阶段结合Tent混沌映射,在探索阶段运用Levy飞行策略提升算法的全局搜索能力.然后,通过改进的GOOSE算法去应用到变分模态分解(VMD)中,得到k和α最优值.对GOOSE算法的改进提高了呼吸心率的检测的精度.最后,该实验结果表明:利用改进的GOOSE优化算法对VMD算法进行参数自适应优化,通过对信号的重构后分析,有效地去除了噪声分量,提高了分解效率.
Abstract
This paper relies on the 60 GHz frequency modulation continuous wave(FMCW)millimeter wave radar,uses the improved GOOSE algorithm,combines tent chaos mapping in the population initialization stage,and uses the Levy flight strategy in the exploration stage to improve the global search capability of the algorithm.Then,the improved GOOSE algorithm is applied to variational mode decomposition(VMD)to obtain the optimal values of k and α.Improvements of the GOOSE algorithm improve the precision of respiratory heart rate detection.Finally,the experimental results show that the improved GOOSE optimization algorithm is used to adaptively optimize the parameters of the VMD algorithm.Through the post-reconstruction analysis of the signal,the noise component is effectively removed and the decomposition efficiency is improved.
关键词
GOOSE算法/变分模态分解/Tent混沌映射/Levy飞行策略/呼吸/心率Key words
GOOSE algorithm/VMD/Tent chaos mapping/Levy flight strategy/respiration/heart rate引用本文复制引用
基金项目
2022年度湖南省教育厅科学研究项目(22C0318)
出版年
2024
NETL
NSTL
万方数据