2134/17386 Matt Best Matt Best Linear MIMO model identification using an extended Kalman filter Loughborough University 2015 System identification Kalman filter Linear model MIMO Model order reduction Engineering not elsewhere classified 2015-04-29 13:13:44 Conference contribution https://repository.lboro.ac.uk/articles/conference_contribution/Linear_MIMO_model_identification_using_an_extended_Kalman_filter/9223106 Linear Multi-Input Multi-Output (MIMO) dynamic models can be identified, with no a priori knowledge of model structure or order, using a new Generalised Identifying Filter (GIF). Based on an Extended Kalman Filter, the new filter identifies the model iteratively, in a continuous modal canonical form, using only input and output time histories. The filter’s self-propagating state error covariance matrix allows easy determination of convergence and conditioning, and by progressively increasing model order, the best fitting reduced-order model can be identified. The method is shown to be resistant to noise and can easily be extended to identification of smoothly nonlinear systems.