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49th IEEE Conference on Decision and Control (CDC)

DOI: 10.1109/cdc.2010.5717191

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Identification of Mixed Linear/Nonlinear State-Space Models

Proceedings article published in 2010 by Fredrik Lindsten, Thomas B. Schön ORCID
This paper is available in a repository.
This paper is available in a repository.

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Abstract

The primary contribution of this paper is an algorithm capable of identifying parameters in certain mixed linear/nonlinear state-space models, containing conditionally linear Gaussian substructures. More specifically, we employ the standard maximum likelihood framework and derive an expectation maximization type algorithm. This involves a nonlinear smoothing problem for the state variables, which for the conditionally linear Gaussian system can be efficiently solved using a so called Rao-Blackwellized particle smoother (RBPS). As a secondary contribution of this paper we extend an existing RBPS to be able to handle the fully interconnected model under study.