Fusion of Data from Different Sources
2009 (English)Report (Other academic)
The use of data from different, often complementary sources in order to obtain a better estimate of the state of the system under consideration has recently become very popular within many scientific areas. We will in this talk provide a framework, including the popular Kalman and particle filters for fusing data from different, complementary sources. The theory will be illustrated using several application examples from the automotive and the aerospace industry. Possible applications for 3D analysis of human motion will be discussed.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. , 3 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2874
Sensor fusion, Nonlinear estimation, Kalman filter, Particle filter
IdentifiersURN: urn:nbn:se:liu:diva-56190ISRN: LiTH-ISY-R-2874OAI: oai:DiVA.org:liu-56190DiVA: diva2:316981