Particle Filtering for Quantized Sensor Information
2005 (English)Report (Other academic)
The implication of quantized sensor information on filtering problems is studied. The Cramer-Rao lower bound (CRLB) is derived for estimation and filtering on quantized data. A particle filter (PF) algorithm that approximates the optimal nonlinear filter is provided, and numerical experiments show that the PF attains the CRLB, while second-order optimal Kalman filter (KF) approaches can perform quite bad.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2005. , 6 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2700
Cramér-Rao lower bound, Particle filter, Kalman filter
IdentifiersURN: urn:nbn:se:liu:diva-56037ISRN: LiTH-ISY-R-2700OAI: oai:DiVA.org:liu-56037DiVA: diva2:316909