A new subspace algorithm consistently identifies stochastic state space models directly from given output data, using only semi-infinite block Hankel matrices. Ala~raet-In this paper, we derive a new subspace algorithm to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semi-infinite block Hankel matrices. The algori...

This article presents an application of the multivariable Model-based Predictive Controller associated to a Kalman filter to damp the mechanical vibrations of a flexible one-link manipulator using state variables feedback. The flexible manipulator, seen as a single input multiple outputs system is modelled using the Lagrange equations. The state space equations are expressed in order to simplif...

-Target tracking is one of the major aspects often used in sonar applications, surveillance systems, communication systems, embedded applications etc. To obtain kinematic components of a moving target such as position, velocity, and acceleration, one of the most used approaches in target tracking is stochastic estimation approach. Movement of the target is described by state space dynamic syste...

In this paper a new type of filter, called the H2 / H∞ FIR filter, is proposed for discretetime state space signal models. The proposed filter requires linearity, unbiased property, FIR structure, and independence of the initial state information in addition to the performance criteria in both H2 and H∞ sense. It is shown that H2, H∞, and H2 / H∞ FIR filter design problems can be converted into...

This paper deals with the design of piecewise linear H∞ filters for discrete-time piecewise linear systems. Both the system and filter have structures that change according to their switching rules. The system switching rule is characterized in terms of a given partition of the system state-space. The filter switching rule is also characterized in terms of a suitable filter state-space partitio...

The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recently developed method, the particle filter, is studied that is based on stochastic simulation. Unlike the well-known extended Kalman filter, the particle filter is applicable to highly nonlinear models with non-Gaussian uncertainties. Recently developed techniques that improve the convergence of the...

This paper presents first and second order convergence analysis of the sparsity aware l0-RLS adaptive filter. The theorems 1 and 2 state the steady state value of mean and mean square deviation of the adaptive filter weight vector.