Design of RLS Wiener fixed-lag smoother using covariance information in linear discrete stochastic systems

In this paper, we propose a new design for the recursive least-squares (RLS) Wiener fixed-lag smoother and filter in linear discrete-time wide-sense stationary stochastic systems. It is assumed that the signal is observed with additive white observation noise. The signal is uncorrelated with the observation noise. The estimators require knowledge of the system matrix, the observation matrix and the variance of the state vector. These quantities can be obtained from the auto-covariance function of the signal. In the estimation algorithms, moreover, the variance of the observation noise is assumed to be known, as a priori information.     

Unscented filtering algorithm using two-step randomly delayed observations in nonlinear systems

An unscented filtering algorithm is derived for a class of nonlinear discrete-time stochastic systems using noisy observations which can be randomly delayed by one or two sample times. The update and the possible delays (of one and two sampling times) of any observation are modelled by using three Bernoulli random variables such that only one of them takes the value one. The algorithm performs in two-steps, prediction and update, and it uses a scaled unscented transformation to approximate the conditional mean and covariance of the state and observation at each time. The performance of the proposed filter is shown in a simulation example which uses a growth model with randomly delayed observations; in this example, the proposed filter is compared with the extended one obtained by linearizing the state and the observation equations and, also, with the unscented Kalman filter. A clear superiority of the proposed filter over the others is inferred.     

Design of a fixed-interval smoother using covariance information based on the innovations approach in linear discrete-time stochastic systems

This paper describes a design for a recursive least-squares Wiener fixed-interval smoother using the covariance information in linear discrete-time stochastic systems. The estimators require information from the observation matrix, the system matrix for the state variable, related to the signal, the variance of the state variable, the cross-variance function of the state variable with the observed value and the variance of the white observation noise. It is assumed that the signal is observed with additive white noise.     

Design of RLS fixed-lag smoother using covariance information in linear discrete stochastic systems

This paper newly designs the recursive least-squares fixed-lag smoother using the covariance information in linear discrete-time stochastic systems. It is assumed that the signal is observed with additive white observation noise and the signal is uncorrelated with the observation noise. The fixed-lag smoother uses the covariance function of the signal in the semi-degenerate kernel form and the variance of the observation noise. The proposed fixed-lag smoother is suitable for the estimations of stationary or non-stationary stochastic signals generally.     

Extended and unscented filtering algorithms using one-step randomly delayed observations

In this paper, the least squares filtering problem is investigated for a class of nonlinear discrete-time stochastic systems using observations with stochastic delays contaminated by additive white noise. The delay is considered to be random and modelled by a binary white noise with values of zero or one; these values indicate that the measurement arrives on time or that it is delayed by one sampling time. Using two different approximations of the first and second-order statistics of a nonlinear transformation of a random vector, we propose two filtering algorithms; the first is based on linear approximations of the system equations and the second on approximations using the scaled unscented transformation. These algorithms generalize the extended and unscented Kalman filters to the case in which the arrival of measurements can be one-step delayed and, hence, the measurement available to estimate the state may not be up-to-date. The accuracy of the different approximations is also analyzed and the performance of the proposed algorithms is compared in a numerical simulation example.     

RLS fixed-lag smoother using covariance information in linear continuous stochastic systems

This paper newly designs the recursive least-squares fixed-lag smoother using the covariance information in linear continuous-time stochastic systems. It is assumed that the signal is observed with additive white observation noise and the signal is uncorrelated with the observation noise. The fixed-lag smoother uses the covariance function of the signal in the semi-degenerate kernel form and the variance of the observation noise. The proposed fixed-lag smoother is appropriate for the estimations of stationary or non-stationary stochastic signals generally.     

Design of recursive least-squares fixed-lag smoother using covariance information in linear continuous stochastic systems

This paper newly designs the recursive least-squares (RLS) fixed-lag smoother and filter using the covariance information in linear continuous-time stochastic systems. It is assumed that the signal is observed with additive white observation noise and the signal is uncorrelated with the observation noise. The estimators require the covariance information of the signal and the variance of the observation noise. The auto-covariance function of the signal is expressed in the semi-degenerate kernel form.     

Feedback control based on discrete-time state observations on synchronization of stochastic impulsive coupled systems

In this paper, feedback control based on discrete-time state observations is used to study the inner synchronization of stochastic impulsive coupled systems (SICSs). Therein, the coupling strength of SICSs is state-dependent switching and time-varying under each switching. Besides, by means of average impulsive interval approach, the Lyapunov method and the graph theory, a synchronization criterion of SICSs is presented. As an application, stochastic impulsive coupled Chua’s circuits with state-dependent switching coupling strength are investigated for the first time and some sufficient conditions are given. Finally, in order to illustrate the effectiveness of our main results, some numerical simulations are presented.     

Stability of neutral stochastic delayed systems with switching and distributed-delay dependent impulses

This paper is devoted to discuss the exponential stability in mean square of neutral stochastic delayed systems (NSDDs) with switching and distributed-delay dependent impulses. By using multiple Lyapunov functions and average dwell time (ADT), we provide some sufficient conditions for the exponential stability in mean square for NSDDs with switching and distributed-delay dependent impulses. Compared with the existing related works, we consider not only the influences of switches and neutral type on the stability of NSDDs with switching and distributed-delay dependent impulses but also the influences of both the stable continuous dynamics case and the stable discrete dynamics case. Finally, we provide two examples to illustrate the effectiveness of the theory.     

Mean-square analysis of stochastic cycles in nonlinear discrete-time systems with parametric noise

We study a discrete-time nonlinear dynamical system forced by parametric noise. A method of mean-square analysis of the dispersion of random solutions near deterministic cycle is elaborated. A problem of the existence of the stable periodic solution of the closed system for second moments is studied in detail. This problem is reduced to the estimation of the spectral radius of some positive operator. A constructive method of the spectral majorants is suggested. The accuracy of our mathematical technique is demonstrated in the analysis of stochastically forced periodic regimes for the Henon model.