Robust Extended Kalman Filtering for Systems With Measurement Outliers

Stochastic Robust Kalman Filtering for Linear Time-Varying Systems with a Multiplicative Measurement Noise

In this paper, a stochastic robust Kalman filtering problem is investigated for timevarying linear systems with stochastic uncertainties in its measurement matrix. The influence of parametric uncertainties on the nominal Kalman filter estimate is analyzed in the sense of classical weighted least-squares criterion. Stochastic approximation of estimation errors due to uncertainties allows us to o...

Kalman Filtering Algorithm in Presence of Outliers

A Kalman Filtering algorithm which is robust to observational outliers is developed by assuming that the measurement error may come from either one of two normal distributions and that transition between these distribution is governed by a Markov Chain. The state estimate is obtained as a weighted average of the estimates from the two parallel filters where the weights are the posterior probabi...

Extended Kalman Filtering by

Distributed Adaptive High-Gain Extended Kalman Filtering for Nonlinear systems

In this work, we propose a distributed adaptive high-gain extended Kalman filtering approach for nonlinear systems. Specifically, we consider a class of nonlinear systems that are composed of several subsystems interacting with each other via their states. In the proposed approach, an adaptive high-gain extended Kalman filter is designed for each subsystem. The distributed Kalman filters commun...

Robust Recursive Kalman–Filtering

We consider robust recursive filtering in the case of a linear, finite-dimensional and timediscrete state-space model with Euclidean state space. Insisting on recursivity for computational reasons, we come up with a new procedure, the rLS-filter, using a Huberized correction-step in the Kalman-filter recursions. Simulation results for ideal and contaminated data indicate that this procedure ach...