DocumentCode
2536919
Title
A general filter for measurements with any probability distribution
Author
Rosenberg, Yoav ; Werman, Michael
Author_Institution
Inst. of Comput. Sci., Hebrew Univ., Jerusalem, Israel
fYear
1997
fDate
17-19 Jun 1997
Firstpage
654
Lastpage
659
Abstract
The Kalman filter is a very efficient optimal filter, however it has the precondition that the noises of the process and of the measurement are Gaussian. The authors introduce `the general distribution filter´ which is an optimal filter that can be used even where the distributions are not Gaussian. An efficient practical implementation of the filter is possible where the distributions are discrete and compact or can be approximated as such
Keywords
adaptive filters; filtering theory; measurement theory; probability; compact distributions; discrete distributions; general distribution filter; optimal filter; probability distribution; Computer science; Current measurement; Gaussian noise; Information filtering; Information filters; Noise measurement; Probability distribution; State estimation; Time measurement; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on
Conference_Location
San Juan
ISSN
1063-6919
Print_ISBN
0-8186-7822-4
Type
conf
DOI
10.1109/CVPR.1997.609395
Filename
609395
Link To Document