Konferenční příspěvek

Straka O., Duník, J., Šimandl M.

Proceedings of the 2011 American Control Conference

AACC

San Francisco, USA

2011

1825-1830

978-1-4577-0079-8

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

clanek_acc11_SDS.pdf [file]

prezentace_acc11_SDS.pdf [file]

The problem of state estimation of nonlinear stochastic dynamic systems with nonlinear inequality constraints is treated. The paper focuses on a particle filtering approach, which provides an estimate of the state in the form of a probability density function. A new computationally efficient particle filter for the constrained estimation problem is proposed. The importance function of the particle filter is generated by the unscented Kalman filter that is supplemented with a designed truncation technique to accommodate the constraint. The proposed filter is illustrated in a numerical example.