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基于改进GOOSE算法的VMD体征信息研究

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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.
Research on VMD of physical signs information based on improved GOOSE algorithm
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.

GOOSE algorithmVMDTent chaos mappingLevy flight strategyrespirationheart rate

刘贵、徐曦、许中华、谭奥成

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湖南工业大学计算机学院,湖南株洲412000

GOOSE算法 变分模态分解 Tent混沌映射 Levy飞行策略 呼吸 心率

2022年度湖南省教育厅科学研究项目

22C0318

2024

传感器与微系统
中国电子科技集团公司第四十九研究所

传感器与微系统

CSTPCD北大核心
影响因子:0.61
ISSN:1000-9787
年,卷(期):2024.43(9)