A Bayesian source separation method for noisy observations by embedding Gaussian process prior
This study aims to explore the blind source separation of heterogeneous monitoring data in the context of Bayesian inference.In comparison with the conventional independent component analysis(ICA)and second-order blind identification(SOBI)techniques,the Bayesian blind source separation approach can explicitly account for and quantify the measurement errors and uncertainty.More importantly,it directly gives rise to the(posterior)probability density distributions of the source signals,which greatly facilitate the reliability assessment of a structure or its components making use of monitoring data.To accommodate non-i.i.d.temporal structure of the source signals,the Gaussian process kernel function is introduced to define the prior distribution of the unknown sources.With this prior distribution in conjunction with appropriate priors for the mixing matrix and noise,the joint posterior distribution of the three groups of unknown parameters is derived by the Bayesian theorem.The Markov chain Monto Carlo,is then applied to numerically obtain the probabilistic characteristics of the sources,mixing matrix,and noise separately.Examples are provided to compare the proposed method with the ICA technique and the SOBI technique.
Structural response monitoring noisy observation source separation Bayesian inference Gaussian process
C.Xua Y.Q.Nia
Department of Civil and Environmental Engineering,The Hong Kong Polytechnic University,Hung Hom,Kowloon,Hong Kong
国际会议
The 7th World Conference on Structural Control and Monitoring(7WCSCM)(第七届结构控制与监测世界大会)
青岛
英文
1784-1794
2018-07-22（万方平台首次上网日期，不代表论文的发表时间）