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Optimal filters for nonlinear systems are in general difficult to derive/implement. The common approach is to obtain approximate solutions, e.g. based on linearizations along the performed path, as done in extended Kalman filters. However, no optimality properties can be guaranteed using these approximations, not even the stability of the estimation error is ensured. In this paper, a new method is...
This paper deals with recursive state estimation for nonlinear systems. A new set of sigma-points for the unscented Kalman filter is proposed as well as a way to substitute a nonlinear output with a linear one. The importance of the function of the state which must be estimated is also illustrated and the need for taking it into account when designing the state estimator. All the proposed methods...
This paper is concerned with the estimation problem for discrete-time stochastic linear systems with multiple packet dropouts. Based on a recently developed model for multiple-packet dropouts, the original system is transferred to a stochastic parameter system by augmentation of the state and measurement. The optimal full-order linear filter of the form of employing the received outputs at the current...
In this paper, we consider the state estimation problem for a class of nonlinear systems with second degree polynomial nonlinearities with noisy measurements. This class of nonlinearities is chosen based on the fact that they may lead to chaotic behavior for possible later use on chaotic communications applications. An unbiased state estimator is proposed having a quadratic structure and a parametric...
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