A model for nonlinear ltering is considered in which errors in state dynamics and observations are modelled deterministically. Mortensen's deterministic estimator and a minimax estimator are considered. A risk sensitive stochastic lter model with small state and observation noise intensities is also considered. The minimax estimator is obtained in the zero noise intensity limit, using asymptoti...

The problem considered here is state estimation in the presence of bounded process and measurement noise. A new nonlinear state estimator, based on interval analysis and the notion of set inversion, is applied to robot localization and tracking. This estimator evaluates a set guaranteed to contain all values of the state that are consistent with the available observations, given the noise bound...

The Kalman filter is the optimal minimum-variance state estimator for linear dynamic systems with Gaussian noise. In addition, the Kalman filter is the optimal linear state estimator for linear dynamic systems with non-Gaussian noise. For nonlinear systems various modifications of the Kalman filter (e.g., the extended Kalman filter, the unscented Kalman filter, and the particle filter) have bee...

This work presents a novel inverse algorithm to estimate time-varying input forces in nonlinear beam systems. With the system parameters determined, the input forces can be estimated in real-time from dynamic responses, which can be used for structural health monitoring. In the process of input forces estimation, the Runge-Kutta fourth-order algorithm was employed to discretize the state equati...

Development of an adaptive target state estimation algorithm for use with advanced missile guidance laws is presented. The target state estimator employs a linear neural network as the decisionmaking element in a nine-state dynamic model of the target. A Kalman filtering algorithm is used to estimate the neural network weights and the target states. The estimator performance is evaluated in a p...

Abstract In this work, an exponential observer is performed for an exothermal axial dispersion tubular reactor that involves one nonlinear sequential reaction. More precisely, the given state estimator is performed by using bounded observations and properties of the nonlinear set of partial differential equations. It is shown that the proposed observer admits a global unique solution and ensure...

Estimation of the state levels of a discrete-time, finite-state Markov chain hidden in coloured Gaussian noise and subjected to unknown nonlinear distortion is considered. If the nonlinear distortion has almost linear behaviour for small values near zero or for large values, extreme value theory can be applied to the level estimation problem, resulting in simple estimation algorithms. The extre...

A set-valued state estimator for nonlinear dynamic systems is presented. The estimator uses the Galerkin approximation to solve Kolmogorov's equation for the diiusion of a continuous time continuous state non-linear system, as well as for implementing discrete time updates of noisy measurements. This ltering of the state is accomplished for a convex set of distributions simultaneously and a fun...

In this paper, we propose a new multi-rate state estimator for seeking control of hard disk drives, which has a proportional, integral, and discontinuous error feedback structure to improve robustness. Conventional state estimators do not have enough accuracy under the existence of external disturbances and model uncertainty. With the proposed estimator, the seeking control system of hard disk ...

The rejection sampling filter and smoother, proposed by Tanizaki (1996, 1999), Tanizaki and Mariano (1998) and Hürzeler and Künsch (1998), take a lot of time computationally. The Markov chain Monte Carlo smoother, developed by Carlin, Polson and Stoffer (1992), Carter and Kohn (1994, 1996) and Geweke and Tanizaki (1999a, 1999b), does not show a good performance depending on nonlinearity and non...