摘要
为解决MRS(Magnetic Resonance Sounding)信号中乘性噪声干扰的问题,提出了马尔科夫链蒙特卡洛的参数提取方法。建立MRS复包络参数的先验信息模型与似然函数模型,使用马尔科夫链蒙特卡洛(MCMC:Markov Chain Monte Carlo)方法对参数后验分布进行采样与拟合,在后验分布中出现次数最多、权值最大的数据作为参数的最优估计值。通过多组不同噪声条件下的MCMC参数提取结果与非线性拟合方法对比,证明了MCMC方法可在乘性噪声的干扰下进行MRS信号参数提取,准确度高、稳定性强。
In order to solve the problem of multiplicative noise interference in MRS signal,a parameter extraction method based on Markov chain Monte Carlo is proposed.The priori information model and likelihood function model of the complex envelope parameters from ground magnetic resonance(MRS:Magnetic Resonance Sounding)is established.A novel method based on MCMC(Markov Chain Monte Carlo),which sample and fit the posterior distribution of the parameters is used to get the data with the most occurrence times of the posterior distribution.The largest weight of the posterior distribution is used as the optimal estimation value of the parameters.By comparing the extraction results of MCMC parameters under different noise conditions with the nonlinear fitting method,it is proved that MCMC method can extract MRS signal parameters with high accuracy and stability,which is under the interference of multiplicative noise.
作者
郜泽霖
魏晋
蒋川东
刁庶
GAO Zelin;WEI Jin;JIANG Chuandong;DIAO Shu(College of Instrument and Electrical Engineering,Jinlin Unversity,Changchun 130061,China)
出处
《吉林大学学报（信息科学版）》
CAS
2020年第4期509-515,共7页
Journal of Jilin University（Information Science Edition）
基金
吉林大学大学生创新创业基金资助项目(201910183x473)。
