Fundamental Filtering Limitations in Linear Non-Gaussian Systems
2005 (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 Cramér-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, 2005. , 9 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2681
Kalman filters, Linear filters, Cramér-Rao lower bound, Nonlinear filters, Optimal filtering
IdentifiersURN: urn:nbn:se:liu:diva-56025ISRN: LiTH-ISY-R-2681OAI: oai:DiVA.org:liu-56025DiVA: diva2:316920