This paper presents a new approach to guaranteed state estimation for non linear discrete-time systems with a bounded description of noise and parameters. The main result is an algorithm to compute a set that contains the states consistent with the measured output and the given noise and parameters. This set is represented by a zonotope. The volume of the zonotope is minimized each sample insta...

This chapter deals with the description, the parametric estimation, the state estimation, and the parametric and state estimation conjointly of nonlinear systems. The focus is on the class of nonlinear systems, which are described by Wiener state-space discrete-time mathematical models. Thus, the authors develop a new recursive parametric estimation algorithm, which is based on least squares te...

This paper considers parameter estimation for nonlinear and non-Gaussian state-space models with correlation. We propose an asymptotic quasilikelihood (AQL) approach which utilises a nonparametric kernel estimator of the conditional variance covariances matrix Σt to replace the true Σt in the standard quasi-likelihood. The kernel estimation avoids the risk of potential miss-specification of Σt ...

In this paper, the problem of joint time-delay and frequency estimation of multiple sinusoidal signals received at two separated sensors is addressed. By formulating the estimation problem with the use of the parallel factor analysis framework, the corresponding state transition and observation matrices are updated in an iterative manner according to alternating least squares, from which the ti...