Research on Unknown Static Parameter Estimation Problem of State Space Models Based on Particle Filter Algorithm

摘要：

　　A novel parameter estimation method for unknown static parameters of the state space model using particle filtering (PF) has proposed in this paper.Traditional methods enlarge state vector by treating the unknown parameter θ as a part of state vector (xk,θ).But this may cause the degeneration of θ,when some estimates become too small to continue as a result of the non-dynamic character of parameters if θ at time k is only determined by time k-1.Compared to traditional methods,this novel method assumes that the posterior distribution of θ is given by previous observation and state vectors,z1∶k and x1∶k.Obtain statistics at time k by using the integration of z1∶k and x1∶k,and solve parameter estimation problem by updating θ recursively.Good results are obtained when this method is used in different models.

关键词： Particle filter State-space models Parameters estimation Sequential updating

作者: JIAO Shang-bin QIAN Fu-cai YANG Jun

作者单位: School of Automation and Information Engineering, XianUniversity of Technology, Xian 710048, China

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1820-1824

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）