Robust maximum-likelihood parameter estimation of stochastic state-space systems based on EM algorithm

O1; This paper addresses the problems of parameter estimation of multivariable stationary stochastic systems on the basis of observed output data. The main contribution is to employ the expectation-maximisation (EM) method as a means for computation of the maximum-likelihood (ML) parameter estimation of the system. Closed form of the expectation of the studied system subjected to Gaussian distribution noise is derived and paraneter choice that maximizes the expectation is also proposed. This results in an iterative algorithm for parameter estimation and the robust algorithm implementation based on technique of QR-factorization and Cholesky factorization is also discussed. Moreover, algorithmic properties such as non-decreasing likelihood value, necessary and sufficient conditions for the algorithm to arrive at a local stationary parameter, the convergence rate and the factors affecting the convergence rate are analyzed. Simulation study shows that the proposed algorithm has attractive properties such as numerical stability, and avoidance of difficult initial conditions.