This paper deals with Kalman filter-based enhancement of a speech signal contaminated by a white noise, using a single microphone system. Such a problem can be stated as a realization issue in the framework of identification. For such a purpose we propose to identify the state space model by using subspace non-iterative algorithms based on orthogonal projections. Unlike Estimate-Maximize (EM)-b...

Modelling multiple-input multiple-output petrochemical industrial dynamic systems is a complex task. Empirical models, based on linear state-space dynamic models often provide a sufficient degree of approximation in a statistically efficient way (i.e. with a small number of parameters). The use of subspace identification methods (SIM) proved to be an useful tool to estimate state-space model pa...

Abstract. This paper deals with modal parameters identification using output-only data. A linear, time-invariant, finite dimensional mechanical system is considered, which is described by a stochastic state-space model excited by unknown operating forces. In this approach, the stochastic state-space model considers the errors due to state-variable and measurements, as integrant parts of the mod...

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 eigenva...

As far as the identification of linear time-invariant state-space representation is concerned, among all of the solutions available in the literature, the subspace-based state-space model identification techniques have proved their efficiency in many practical cases since the beginning of the 90’s as illustrated, e.g., in [95, 12, 4, 30, 51, 28, 68, 27, 89, 11]. This paper introduces an overvie...

We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output–MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient fo...

In this paper, a subspace identification solution is provided for Active Noise Control (ANC) problems. The solution is related to so-called block updating methods, where instead of updating the (feedforward) controller on a sample by sample base, it is updated each time based on a block of N samples. The use of the subspace identification based ANC methods enables non-iterative derivation and u...

An extended state space (ESS) model, familiar in subspace identification theory, is used for the development of a model based predictive control algorithm for linear model structures. In the ESS model, the state vector consists of system outputs, which eliminates the need for a state estimator. A framework for model based predictive control is presented. Both general linear state space model st...