Fundamental Filtering Limitations in Linear Non-Gaussian Systems
2004 (English)Report (Other academic)
The Kalman filter is known to be the optimal linear filter for linear non-Gaussian systems. However, nonlinear filters such as Kalman filter banks and more recent numerical methods such as the particle filter are sometimes superior in performance. Here a procedure to a priori decide how much can be gained using nonlinear filters, without having to resort to Monte Carlo simulations, is outlined. The procedure is derived in terms of the posterior Cramer-Rao lower bound. Results are shown for a class of standard distributions and models in practice.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2004. , 8 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2640
Kalman filters, Linear filters, Cramer-rao lower bound, Nonlinear filters, Optimal filtering
IdentifiersURN: urn:nbn:se:liu:diva-56005ISRN: LiTH-ISY-R-2640OAI: oai:DiVA.org:liu-56005DiVA: diva2:316727