Propagator-based methods for recursive subspace model identification

The problem of the online identification of multi-input multi-output (MIMO) state-space models in the framework of discrete-time subspace methods is considered in this paper. Several algorithms, based on a recursive formulation of the MIMO Output Error State-Space (MOESP) identification class, are d...

Full description

Saved in:
Bibliographic Details
Published in:Signal processing 2008-03, Vol.88 (3), p.468-491
Main Authors: Mercère, Guillaume, Bako, Laurent, Lecœuche, Stéphane
Format: Article
Language:English
Subjects:
Applied sciences
Automatic
Detection, estimation, filtering, equalization, prediction
Engineering Sciences
Exact sciences and technology
Information, signal and communications theory
Instrumental variable
Miscellaneous
Multivariable system
Recursive algorithm
Signal and communications theory
Signal processing
Signal, noise
Subspace method
System identification
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The problem of the online identification of multi-input multi-output (MIMO) state-space models in the framework of discrete-time subspace methods is considered in this paper. Several algorithms, based on a recursive formulation of the MIMO Output Error State-Space (MOESP) identification class, are developed. The main goals of the proposed methods are to circumvent the huge complexity of eigenvalues or singular values decomposition techniques used by the offline algorithm and to provide consistent state-space matrices estimates in a noisy framework. The underlying principle consists in using the relationship between array signal processing and subspace identification to adjust the propagator method (originally developed in array signal processing) to track the subspace spanned by the observability matrix. The problem of the (coloured) disturbances acting on the system is solved by introducing an instrumental variable in the minimized cost functions. A particular attention is paid to the algorithmic development and to the computational cost. The benefits of these algorithms in comparison with existing methods are emphasized with a simulation study in time-invariant and time-varying scenarios.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2007.09.012