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AUTOMATICA
2005

Robust maximum-likelihood estimation of multivariable dynamic systems

13 years 11 months ago
Robust maximum-likelihood estimation of multivariable dynamic systems
This paper examines the problem of estimating linear time-invariant state-space system models. In particular it addresses the parametrization and numerical robustness concerns that arise in the multivariable case. These difficulties are well recognised in the literature, resulting (for example) in extensive study of subspace based techniques, as well as recent interest in "data driven" local co-ordinate approaches to gradient search solutions. The paper here proposes a different strategy that employs the Expectation Maximisation (EM) technique. The consequence is an algorithm that is iterative, and locally convergent to stationary points of the (Gaussian) Likelihood function. Furthermore, theoretical and empirical evidence presented here establishes additional attractive properties such as numerical robustness, avoidance of difficult parametrization choices, the ability to estimate unstable systems, the ability to naturally and easily estimate non-zero initial conditions, an...
Stuart Gibson, Brett Ninness
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2005
Where AUTOMATICA
Authors Stuart Gibson, Brett Ninness
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