Konferenční příspěvek

Duník J., Straka O., Šimandl M.

Proceedings of the 18th IFAC World Congress

Elsevier

Milano, Italy

2011

8-13

1474-6670

Ing. Ondřej Straka, Ph.D. (FAV Západočeská univerzita, katedra kybernetiky)

State estimation; Estimation theory; Kalman filtering

clanek_ifac11.DSS.pdf [file]

prezentace_ifac11_DSS.pdf [file]

The paper deals with state estimation of nonlinear stochastic dynamic systems. Traditional filters providing local estimates of the states, such as the extended Kalman filter, unscented Kalman filter or the cubature Kalman filter, are based on approximations which lead to biased estimates of the state and measurement statistics. The aim of the paper is to propose a new local filter that utilises a randomised unscented transformation which is a special case of stochastic integration rules providing an unbiased estimate of an integral. The new filter provides estimates of higher quality than the traditional filters and renders a randomised version of the unscented Kalman filter. The proposed filter is illustrated in a numerical example.