Joint State and Parameter Estimation for Stationary ARMA Model with Unknown Noise Model
作者单位：School of automationBeijing Institute of Technology School of mechanical engineeringBeijing Institute of Technology
会议名称：《第36届中国控制会议》
会议日期：2017年
学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学]
基 金：supported by National Natural Science Foundation(NNSF) of China under Grant 6160303040
关 键 词：EM algorithm Gaussian mixture model non-Gaussian noise Particle filter Stationary ARMA model
摘 要：The parameter estimation of a wide-sense auto-regressive moving-average（ARMA） model,which is widely applied into a variety of fields,is an extremely important research *** research is conducted with the known driving environment noise or assuming that the driving noise consists unknown *** the driving noise is really complex in *** now,less attention on parameter estimation for a wide-sense stationary hidden ARMA process with unknown noise is paid attention,although it is very common in the complex control *** paper presents parameter estimation method for hidden wide-sense ARMA processes with the known model order.A dual particle filter-based method is adopted to estimate joint states and *** method can be divided into two *** first step utilizes the particle filter algorithm to estimate the state of an ARMA model,then conduct the estimation of parameters in the PF algorithm on the basis of state estimation in the second *** the noise model is extremely unknown,the Gaussian mixture model is adopted to approach the posterior probability function in the process of the above dual PF algorithm according to EM *** results verify the effectiveness of the proposed scheme.