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This page is a copy of research/systems_and_control/signalproc/topics/sys_id (Wed, 31 Aug 2022 15:09:08)

Fundamental problems in systems identification

A brief description of the research problems under current focus is as follows.

Partial least squares (PLS)
Study of the statistical properties of PLS approach to parameter estimation of linear regressions
Cramér-Rao bounds under parameter constraints
Derivation of such bounds and applying them to problems in blind channel identification/equalisation.
Signal separation by using system identification tools
Separating dynamic mixtures of two signals (e.g. two speech signals) by means of system identification and spectral analysis techniques.
Identification of poorly excited systems
Using subspace-based techniques to prevent ill-conditioning when the input to the system under study is poorly exciting, and application to stereophonic echo cancellation.
Biased regression
New biased estimators (based, among other things, on cross validation) with better performance in ill conditioned problems than the commonly used unbiased methods.
Cramér-Rao bounds for general state-space models
Algorithms are developped for computing such bounds from discrete-time data of stochastic state-space models given either in continous or discrete time.
Identification of systems in closed loop
Instrumental variable algorithms are analysed as an alternative to some bias-compensated least squares estimators.

Selected references:

[[OPUS style=numbered;pubids=29657,29695,29724]]

Updated  2022-08-31 15:09:08 by Victor Kuismin.